WO2023098289A1 - 一种基于对抗学习的无标签胰腺影像自动分割系统 - Google Patents

一种基于对抗学习的无标签胰腺影像自动分割系统 Download PDF

Info

Publication number
WO2023098289A1
WO2023098289A1 PCT/CN2022/124228 CN2022124228W WO2023098289A1 WO 2023098289 A1 WO2023098289 A1 WO 2023098289A1 CN 2022124228 W CN2022124228 W CN 2022124228W WO 2023098289 A1 WO2023098289 A1 WO 2023098289A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
pancreas
pancreatic
unlabeled
image data
Prior art date
Application number
PCT/CN2022/124228
Other languages
English (en)
French (fr)
Inventor
李劲松
田雨
周天舒
朱琰
Original Assignee
浙江大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 浙江大学 filed Critical 浙江大学
Publication of WO2023098289A1 publication Critical patent/WO2023098289A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4046—Scaling of whole images or parts thereof, e.g. expanding or contracting using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4053—Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/002—Image coding using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Definitions

  • the invention belongs to the technical field of image data processing, and in particular relates to an automatic segmentation system of unlabeled pancreas images based on adversarial learning.
  • Pancreas-related diseases develop rapidly clinically and have poor prognosis.
  • Pancreatic cancer is a highly malignant tumor of the digestive tract with a 5-year survival rate of less than 5%. Early detection of pancreatic lesions and precise treatment are crucial to improving the quality of life of patients with pancreatic-related diseases.
  • Pancreatic cancer treatment options currently mainly include surgical resection and neoadjuvant therapy, both of which require precise positioning of pancreatic tissue before surgery.
  • Abdominal computed tomography (Computed Tomography) is an important examination method in the diagnosis process of pancreas-related diseases.
  • Pancreas CT image automatic segmentation tool can assist radiologists to quickly locate the position and outline of the pancreas, save doctors' time for film reading, and speed up patient diagnosis and treatment. It is of great significance for early screening and subsequent treatment of pancreas-related diseases.
  • Two-dimensional pancreatic image segmentation mostly adopts a two-step segmentation method from coarse-to-fine (Coarse-to-Fine), and segments different fine-grained regions of interest (ROI) in pancreatic images at different stages, but this method lacks error correction Mechanism, the error in the coarse segmentation stage will be introduced into the fine segmentation stage, resulting in distortion of the segmentation results.
  • 3D pancreatic image segmentation can use the 3D axial continuity information and tissue anatomy information of CT images.
  • the segmentation network mostly uses U-Net or ResNet and their corresponding improved networks. Through skip connections and The residual structure performs cascade fusion of different scale features to enhance the automatic segmentation model's ability to recognize small target objects such as pancreas.
  • pancreatic CT images have the characteristics of unclear borders and low contrast with adjacent tissues. .
  • the labeling of pancreatic CT images requires multiple experts and doctors to verify the results with each other, and the cost and time of labeling are quite expensive. Therefore, the labeling data of pancreatic CT images is scarce, which brings certain limitations to the construction of automatic pancreas segmentation models.
  • Segmentation models trained directly using public datasets of pancreatic images or labeled datasets of pancreatic images often fail to perform automatic segmentation on unlabeled pancreatic image data.
  • a major technical problem in promotion Due to differences in patient populations, imaging equipment, imaging protocols, and imaging parameters among different medical centers, there is also heterogeneity in the scanned CT images. The existence of heterogeneity in pancreatic images makes it difficult to generalize the automatic segmentation model of pancreatic images, which makes it difficult to use a large amount of unlabeled pancreatic image data generated clinically, and it is difficult to construct a pancreatic image segmentation model to assist clinicians.
  • the present invention focuses on the use of confrontational learning methods to find common image features in labeled image data and unlabeled image data in the absence of pancreatic image data annotation in medical centers, and strengthen the personalized image features of unlabeled image data. Construct a reliable pancreatic image segmentation model suitable for unlabeled pancreatic CT image data, assist radiologists to read and diagnose, and improve the generalization of models in the field of medical big data analysis.
  • the present invention introduces the Transformer structure to automatically segment pancreatic CT images, performs pixel block partition processing on pancreatic CT image data, and adds a self-attention mechanism to establish long-connected cross-correlation relationships between pixel blocks, and encodes in multiple stages
  • the residual structure is used in the decoder-decoder structure, so that the multi-scale pancreatic target image features can be weighted and interacted, which can significantly improve the segmentation effect of small targets such as pancreatic tissue.
  • a non-labeled pancreatic image automatic segmentation system based on adversarial learning the system includes the following modules:
  • Data screening module used to collect and screen pancreatic CT image data
  • Data quality alignment module used for image normalization preprocessing of pancreatic CT image data from different data sources
  • Transfer learning module including a segmentation module for building a pancreatic image segmentation model, and an adversarial learning module for adversarial learning of image features between different data sources;
  • the pancreas image segmentation model constructed in the segmentation module uses a multi-stage encoder-decoder structure, the encoder adopts a Transformer, and the multi-stage encoder abstracts the feature layer by layer to obtain multi-scale pancreas target image features, and the multi-scale pancreas target
  • the image features are introduced into the decoder of the corresponding stage through the residual connection, and the target segmentation feature decoding calculation is performed to obtain the three-dimensional feature map of the corresponding scale, and the multi-stage decoder finally outputs the segmentation mask;
  • the pancreas image segmentation model trained with labeled image data is used as the initial pancreas image segmentation model corresponding to the unlabeled image data, and the multi-scale pancreas target image features of the labeled image data and unlabeled image data are extracted, and Through discriminator confrontation training, update the pancreas image segmentation model corresponding to unlabeled image data;
  • the discriminator is a three-dimensional multi-scale progressive feature fusion discriminator, and the discriminator has a plurality of entrances, which respectively input the three-dimensional feature maps output by the decoders of multiple stages in the segmentation module, and each feature map is dimensionally reduced After the operation, the splicing operation is performed with the feature map of the next scale. After the discriminator completes the feature fusion of the multi-scale feature map, it outputs the prediction results of different data sources, calculates the discriminator loss function corresponding to the real data source label, and updates the discriminator weight. .
  • the input of the pancreatic image segmentation model is all pixel blocks obtained by performing pixel block partitioning on the original pancreatic CT image
  • the first-stage encoder is composed of a linear transformation operation and a Swin Transformer Block
  • the subsequent stage encoder is composed of a pixel block combination operation and a Swin Transformer Block
  • the linear transformation operation is used to convert the pixel block into a serialized feature vector
  • the The pixel block combination operation is used to combine several adjacent pixel blocks and downsample
  • the Swin Transformer Block consists of a multi-head sliding window self-attention module MSA and a multi-layer perception module MLP, each MSA and MLP front Both are connected with a LayerNorm layer, and use a residual connection after each MSA and MLP, and the Swin Transformer Block obtains a feature map related to the relative position of the pixel;
  • the decoder consists of an upsampling operation consisting of a 3D transposed convolution layer and an activation function layer, and a decoding module consisting of several stacked 3D convolution layers and activation function layers.
  • the process of training the pancreas image segmentation model according to the labeled image data includes:
  • the pancreas image segmentation model in the segmentation module is input in pairs, n is the total number of samples in S, and the pancreas image segmentation model is optimized based on the assumption that the weight of the input x i mapping label data y i with the smallest error is obtained, and the total
  • the transfer learning module after obtaining the pancreas image segmentation model corresponding to the labeled image data After that, the pancreas image segmentation model corresponding to the unlabeled image data initialized to During discriminator confrontation training, The parameters are always frozen unchanged, The parameters are constantly updated.
  • the adversarial training of the discriminator includes the following steps:
  • the labeled image data set S will be the data input model
  • Obtain image features of multi-scale pancreas target Denote the unlabeled image dataset as z ⁇ T(z), where z is the unlabeled pancreas CT image data, and the unlabeled image dataset T is the data input model
  • the Transformer structure is used as the feature encoder in the design of the pancreatic image segmentation model, and a multi-stage encoder-decoder structure is designed.
  • the advantage of the Transformer structure is that it is not necessary to design a deep network layer to adapt the model to the feature map that gradually reduces the dimensionality, but to improve the encoder's ability to understand the image by partitioning the pixel blocks and establishing the relationship between the pixel blocks.
  • the low-level feature learning ability of pancreas images the network memory access is significantly reduced, and the operation speed is accelerated; the residual structure connection is used between the multi-stage encoder and decoder, and the cascade operation is performed on the multi-scale pancreas target image features to effectively solve the pancreas volume. Small problems with complex and variable structures.
  • the present invention is oriented to the actual application scene of the medical center, aiming at the problem of poor generalization performance of the existing pancreatic image segmentation model when there is heterogeneity in the image data, the unlabeled pancreatic CT in the local image database of the medical center does not need medical personnel
  • a pancreatic image segmentation model suitable for unlabeled pancreatic CT images is constructed, which realizes the automatic segmentation of a large number of unlabeled pancreatic CT image data in the local image database of the medical center, and provides a variety of reliable and Instructive visualization of image results and structured chart information to display segmentation results can effectively shorten the time for doctors to read images, optimize the diagnosis and treatment process of pancreas-related diseases, and improve the efficiency of doctors' diagnosis and treatment.
  • FIG. 1 is a frame diagram of an automatic segmentation system for unlabeled pancreatic images based on adversarial learning provided by an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a pancreas image segmentation model constructed in the segmentation module provided by an embodiment of the present invention
  • Fig. 3 is a schematic structural diagram of the discriminator constructed in the confrontation learning module provided by the embodiment of the present invention.
  • Fig. 4 is the workflow diagram of the migration learning module provided by the embodiment of the present invention.
  • Fig. 5 is a flow chart of system usage in the result display module provided by the embodiment of the present invention.
  • An embodiment of the present invention provides an automatic segmentation system for unlabeled pancreatic images based on adversarial learning.
  • the system includes a data screening module, a data quality alignment module, a transfer learning module, and a result display module. The implementation process of the module.
  • the main task of the data screening module is to screen the pancreas CT image data that meet the research conditions from the local image database of the medical center according to the research task.
  • the screening parameters can be set by the system user, including the basic characteristics of the research samples (age, gender, visit to doctor, etc.). Time, etc.), the time of CT image shooting of the samples enrolled in the study, the shooting equipment, the health status of the pancreas, and the types of pancreatic diseases, etc.
  • the pancreatic CT image data is queried and extracted from the local image database of the medical center, and converted into .nii format or .nii.gz format for subsequent segmentation according to actual needs.
  • the data quality alignment module performs image normalization preprocessing on pancreatic CT image data to reduce the data heterogeneity among different sources of pancreatic CT image data, and selects the selected pancreatic CT image data from the region of interest screening, super-resolution reconstruction, Data amplification, gray level normalization, etc. are used for quality alignment. For the labeled pancreatic CT image data, the same normalization preprocessing is performed on the labels.
  • the specific implementation method is:
  • Super-resolution reconstruction for the filtered pancreas CT image of the region of interest, super-resolution reconstruction is performed to the preset W*L pixel size on the horizontal plane, and super-resolution reconstruction is performed to the preset slice thickness d in the axial direction, where the super-resolution Three-dimensional linear cube interpolation is used for voxel interpolation for super-resolution reconstruction.
  • Gray scale normalization The effective CT value intensity range of abdominal organs is generally [-160, 240] HU, and the effective CT value intensity range of pancreas is generally [-100, 240] HU.
  • the gray value of the current pancreatic CT image is truncated to [-100, 240], and the gray value of the image is normalized to [0, 1] using the Min-Max normalization method.
  • the transfer learning module mainly includes two sub-modules: a segmentation module for building a pancreatic image segmentation model, and an adversarial learning module for adversarial learning of image features between different data sources.
  • the pancreas image segmentation model uses a multi-stage encoder-decoder structure to improve the situation that the feature semantics in the small target segmentation of the pancreas are not rich; Multi-scale high-dimensional and fine-grained pancreas target image features are obtained.
  • the multi-scale pancreas target image features are connected through the residual (Residual) and are introduced into the decoder of the corresponding stage to decode and calculate the target segmentation features to obtain the corresponding scale.
  • Three-dimensional feature map; the final output of the multi-stage decoder is expressed as the prediction result of identifying each pixel of the input pancreatic CT image as the foreground pancreatic tissue or background, that is, the segmentation mask.
  • the input of the pancreatic image segmentation model is all pixel blocks obtained by performing pixel block partitioning on the original pancreatic CT image
  • the first stage encoder consists of linear transformation operation and Swin Transformer Block
  • the subsequent stage encoder consists of pixel block combination operation and Swin Transformer Block
  • linear transformation operation is used to convert pixel blocks into serialized feature vectors
  • pixel block combination operation It is used to combine and downsample several adjacent pixel blocks, and then input the downsampled pixel blocks into the Swin Transformer Block
  • the Swin Transformer Block consists of a multi-head sliding window self-attention module MSA (Multi-head SelfAttention) and a
  • the multilayer perception module is composed of MLP (Multilayer Perception).
  • Each MSA and MLP is preceded by a LayerNorm layer, and after each MSA and MLP, a residual connection is used, and the Swin Transformer Block obtains a feature map related to the relative position of the pixel.
  • Each stage of the decoder stacks the Swin Transformer Block in multiple layers, so that the model can fully extract the features of the input image.
  • the decoder consists of an upsampling operation and a decoding module Decoder Block.
  • the upsampling operation consists of a three-dimensional transpose convolution (Transpose Convolution) layer and an activation function layer.
  • the Decoder Block consists of several stacked three-dimensional convolution layers and activation function layers.
  • P the size of the obtained pixel block
  • the pancreas image segmentation model gets the final segmentation result through 4 stages of encoder and 4 stages of decoder.
  • Encoder 1 (E_Stage1) is composed of linear transformation operation and Swin Transformer Block
  • encoder 2-4 (E_Stage2-E_Stage4) is composed of pixel block combination operation and Swin Transformer Block.
  • the numbers of Swin Transformer Blocks in encoders 1-4 are 2, 2, 18, and 2, respectively.
  • Decoders 1-4 (DE_Stage1-DE_Stage4) consist of an upsampling operation and a Decoder Block.
  • the linear transformation operation is used to convert the pixel block into a serialized feature vector
  • the Swin Transformer Block is used to obtain the feature map related to the relative position of the pixel
  • the serialized feature vector and the relative position of the pixel prompted by the Swin Transformer Block Combined to obtain the positional relationship between the pixel blocks.
  • Decoder 1-4 consists of an upsampling operation and a Decoder Block.
  • the upsampling operation consists of a three-dimensional transposed convolution layer and an activation function layer PReLU.
  • the three-dimensional transposed convolution stride (stride) is set to 2
  • the Decoder Block is composed of 3 It consists of a stacked three-dimensional convolutional layer and an activation function layer PReLU.
  • the encoder and decoder are connected by residuals between the same stages, and are used for the cascade interaction of features of pancreas target images at 4 scales.
  • the segmentation mask is output by decoder 1 .
  • the adversarial learning module uses the pancreas image segmentation model trained according to the labeled image data as the initial pancreas image segmentation model corresponding to the unlabeled image data, extracts the multi-scale pancreas target image features of the labeled image data and the unlabeled image data, and passes Multi-scale discriminator confrontation training to update the pancreas image segmentation model corresponding to unlabeled image data.
  • the discriminator can adopt a three-dimensional multi-scale progressive feature fusion discriminator.
  • the discriminator has multiple entries, which respectively input the three-dimensional feature maps output by the decoders of multiple stages in the segmentation module.
  • the discriminator has four entries. , as shown in Figure 3, respectively input the three-dimensional feature maps of the four scales output from the segmentation module decoder DE_Stage1-DE_Stage4.
  • Each feature map passes through the three-dimensional convolutional layer and the activation function layer in turn (the activation function layer can use PReLU).
  • the step size of the three-dimensional convolutional layer can be set to 2.
  • the spatial scale of the image features will be reduced to The size of the feature map of the next scale is then concatenated with the feature map of the next scale, and sent to the next scale of the three-dimensional convolutional layer and activation function layer together.
  • the feature maps of multiple scales are fused, they are input into the average pooling layer and the fully connected layer in turn, and the prediction results of different data sources are output, and the discriminator loss function is calculated corresponding to the real data source labels, and the discriminator weights are updated.
  • the specific workflow of the transfer learning module is as follows:
  • the labeled image data set is denoted as (x, y) ⁇ S((x, y)), where x is the labeled pancreatic CT image data, and y is the label corresponding to x.
  • the labeled CT image data of the pancreas can be a CT image with accurate annotation of the pancreas produced by a medical center, or it can be publicly released domestic and foreign labeled pancreas image data.
  • the unlabeled image data set is denoted as z ⁇ T(z), where z is the unlabeled pancreatic CT image data.
  • the pancreas CT image data and label data of the labeled image dataset S The pancreas image segmentation model is input in pairs, and n is the total number of samples in S. At this time, the pancreas image segmentation model is optimized based on the assumption that the weight of the input xi mapping label data y i has the smallest error, and the total loss function L seg is defined as The linear combination of the cross-entropy loss function L CE and the Dice Loss loss function L Dice is expressed as:
  • the cross-entropy loss function L CE is expressed as:
  • the Dice Loss loss function L Dice is expressed as:
  • P is the pancreas area predicted by the pancreas image segmentation model
  • Z is the pancreas area marked by the label y.
  • pancreas image segmentation model corresponding to the labeled image data is denoted as
  • the labeled image data set S will be the data input model Obtain image features of multi-scale pancreas target
  • the unlabeled image dataset T will be the data input model
  • Obtain image features of multi-scale pancreas target k is the total number of samples in T.
  • the discriminator optimization problem is transformed into the problem of minimizing the loss function in the neural network, and the weight parameters of the discriminator are updated using the gradient descent method to obtain the updated discriminator, which is denoted as
  • the discriminator After the discriminator is updated, it remains frozen temporarily.
  • the data of the unlabeled image dataset T The sign is changed to 1, and the data Single branch input current discriminator In , the update gradient is calculated according to the loss function of the discriminator, and backpropagated to the pancreas image segmentation model corresponding to the unlabeled image data Implementation model renew.
  • the result display module post-processes the segmentation mask output by the segmentation module, and displays the pancreas CT image data and pancreas automatic segmentation results.
  • the display content is divided into visual image results and structured chart information.
  • the specific implementation is, as shown in Figure 5, when the unlabeled image data corresponding to the pancreas image segmentation model
  • the system user can select the pancreas CT image data to be studied in the local image database of the medical center through the data screening module, complete the format conversion of the selected pancreatic CT image data, and customize the parameters in the data quality alignment module.
  • the CT image data is subjected to image normalization preprocessing, and the segmentation module is used to segment the preprocessed pancreatic CT image data to obtain a segmentation mask.
  • the segmentation process is to predict each pixel in the pancreatic CT image as the probability value of the target foreground pancreatic tissue or background, so there are usually some isolated points and noise points in the segmentation mask.
  • the present invention uses a conditional random field (CRF) model and The hole filling algorithm further optimizes the segmentation mask to eliminate the cavity structure in the segmentation mask and smooth the edges, and provides visual image results and structured chart information display for the optimized segmentation mask and original pancreatic CT images.
  • CRF conditional random field
  • the visualized image results include but are not limited to the original 3D pancreatic CT image, pancreas 3D segmentation mask, original 3D pancreatic tissue image, pancreas 2D segmentation mask, original 2D pancreatic tissue layered image, etc., and supports mouse dragging, rotation and zooming Operations such as images enable a richer display of results.
  • the structured chart information includes, but is not limited to, the volume of the pancreas, the three-dimensional size of the pancreas, the two-dimensional layered size of the pancreas, and the depth of the pancreas tissue.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

本发明公开了一种基于对抗学习的无标签胰腺影像自动分割系统，本发明利用对抗学习方法寻找有标签影像数据和无标签影像数据中的共性影像特征，并强化无标签影像数据的个性化影像特征，构建适用于无标签胰腺CT影像的胰腺影像分割模型；本发明引入Transformer结构对胰腺CT影像进行分割，通过对胰腺CT影像数据进行像素块分区处理，并加入自注意力机制建立起像素块之间的长连接互相关关系，在多阶段编码器-解码器结构中使用残差结构，让多尺度胰腺目标影像特征能够加权交互，显著提升对胰腺组织这类小目标的分割效果。本发明能够针对无标签胰腺CT影像数据给出可靠的分割结果，有效缩短医生读片时间，优化胰腺相关疾病诊疗流程，提高医生诊疗效率。

Description

一种基于对抗学习的无标签胰腺影像自动分割系统 技术领域
本发明属于图像数据处理技术领域，尤其涉及一种基于对抗学习的无标签胰腺影像自动分割系统。
背景技术
胰腺相关疾病在临床上病情发展快、预后效果差。胰腺癌是一种5年存活率不足5％，恶性程度极高的消化道恶性肿瘤，早期发现胰腺部位病变并实施精准治疗对改善胰腺相关疾病患者生存质量至关重要。胰腺癌治疗方案目前主要有手术切除和新辅助治疗，这两种治疗方案均需要术前对胰腺组织精准定位。腹部计算机断层扫描(Computed Tomography)是胰腺相关疾病诊断流程中的重要的检查方式，胰腺CT影像自动分割工具可以辅助影像科医生快速定位胰腺位置和轮廓，节省医生读片时间，加速患者诊疗流程，对胰腺相关疾病早期筛查和后续治疗意义重大。
随着大数据分析技术的发展，也产生了一些基于深度学习的胰腺CT影像自动分割方案。二维胰腺影像分割多采用由粗到精(Coarse-to-Fine)的两步分割方法，在不同阶段针对不同细粒度的胰腺影像感兴趣区域(ROI)进行分割，但这种方法缺乏纠错机制，在粗分割阶段的误差会被引入细分割阶段导致分割结果失真。三维胰腺影像分割相比于二维胰腺影像分割可以利用CT影像的三维轴向连续性信息和组织解剖学信息，分割网络多采用U-Net或ResNet及二者对应的改进网络，通过跳跃连接和残差结构对不同尺度特征进行级联融合，增强自动分割模型对胰腺这类小目标物体的识别能力。
深度学习模型的优秀性能依靠大数据驱动，胰腺CT影像自动分割工具训练过程需要大量具有精准标注的数据。但胰腺组织位于后腹膜，体积小，结构复杂多变，且紧邻胃、十二指肠、脾脏、大血管等多种组织，胰腺CT影像具有边界不清、与邻近组织间对比度较低等特点。对胰腺CT影像的标注需要多名专家医生互相验证结果，标注费用和时间代价都相当昂贵，因此胰腺CT影像的标注数据匮乏，这对胰腺自动分割模型的构建带来一定限制。
直接使用胰腺影像公共数据集或有标签胰腺影像数据集训练出的分割模型，对无标签胰腺影像数据进行自动分割时效果往往不佳，医学大数据模型的泛化能力是深度学习在医疗领域广泛推广上的一大技术难题。不同医疗中心间由于就诊患者人群、成像设备、成像协议、成像参数等的差异，扫描出的CT影像也存在异质性。胰腺影像异质性的存在使得胰腺影像自动分割模型难以泛化，导致临床产生的大量无标签胰腺影像数据难以被利用起来，难以构建 出辅助临床医生的胰腺影像分割模型。
发明内容
本发明的目的在于针对无标签数据难以被利用的医学大数据困境，提供一种基于对抗学习的无标签胰腺影像自动分割系统，主要解决如下技术问题：
1.本发明重点关注在医疗中心缺少胰腺影像数据标注的情况下，利用对抗学习方法寻找有标签影像数据和无标签影像数据中的共性影像特征，并强化无标签影像数据的个性化影像特征，构建适用于无标签胰腺CT影像数据的性能可靠的胰腺影像分割模型，辅助影像科医生读片诊断，改善医疗大数据分析领域模型的泛化性问题。
2.本发明引入Transformer结构对胰腺CT影像进行自动分割，通过对胰腺CT影像数据进行像素块分区处理，并加入自注意力机制建立起像素块之间的长连接互相关关系，在多阶段编码器-解码器结构中使用残差结构，让多尺度胰腺目标影像特征能够进行加权交互，能够显著提升对胰腺组织这类小目标的分割效果。
本发明的目的是通过以下技术方案实现的：一种基于对抗学习的无标签胰腺影像自动分割系统，该系统包括以下模块：
数据筛选模块：用于采集和筛选胰腺CT影像数据；
数据质量对齐模块：用于对不同数据源的胰腺CT影像数据做影像规范化预处理；
迁移学习模块，包括用于构建胰腺影像分割模型的分割模块、用于不同数据源间影像特征对抗学习的对抗学习模块；
所述分割模块中构建的胰腺影像分割模型使用多阶段编码器-解码器结构，编码器采用Transformer，多个阶段的编码器将特征层层抽象，得到多尺度胰腺目标影像特征，多尺度胰腺目标影像特征通过残差连接，被引入对应阶段的解码器，进行目标分割特征解码计算，得到对应尺度的三维特征图，多个阶段的解码器最终输出分割掩膜；
所述对抗学习模块中，将有标签影像数据训练的胰腺影像分割模型作为无标签影像数据对应的初始胰腺影像分割模型，提取有标签影像数据和无标签影像数据的多尺度胰腺目标影像特征，并通过判别器对抗训练，更新无标签影像数据对应的胰腺影像分割模型；
所述判别器为三维多尺度渐进式特征融合判别器，所述判别器具有多个入口，分别输入所述分割模块中多个阶段的解码器输出的三维特征图，每张特征图经过降维操作后与下一个尺度的特征图进行拼接操作，所述判别器完成多尺度特征图特征融合后，输出不同数据源的预测结果，对应真实数据源标签计算判别器损失函数，并更新判别器权重。
进一步地，所述分割模块中，所述胰腺影像分割模型的输入为由原始胰腺CT影像进行像素块分区得到的所有像素块；
第一阶段编码器由线性变换操作和Swin Transformer Block组成，后续阶段编码器由像素块组合操作和Swin Transformer Block组成；所述线性变换操作用于将像素块转换成序列化的特征向量；所述像素块组合操作用于将若干相邻的像素块进行组合并降采样；所述Swin Transformer Block由一个多头滑动窗口的自注意力模块MSA和一个多层感知模块MLP组成，每个MSA和MLP前均接有LayerNorm层，并在每个MSA和MLP之后使用残差连接，所述Swin Transformer Block得到像素相对位置相关的特征图；
解码器由上采样操作和解码模块组成，所述上采样操作由三维转置卷积层和激活函数层组成，所述解码模块由若干堆叠的三维卷积层和激活函数层组成。
进一步地，所述迁移学习模块中，根据有标签影像数据训练胰腺影像分割模型的过程包括：
将有标签影像数据集记为(x，y)～S((x，y))，其中x为有标签胰腺CT影像数据，y为x对应的标签；将有标签影像数据集S的胰腺CT影像数据和标签数据
Figure PCTCN2022124228-appb-000001
成对输入所述分割模块中的胰腺影像分割模型，n为S中的样本总数，所述胰腺影像分割模型基于得到对于输入x i映射标签数据y i误差最小的权重的假设进行优化，将总损失函数L seg定义为交叉熵损失函数L CE和Dice Loss损失函数L Dice的线性组合L seg＝L CE+λL Dice，λ为线性权重系数；经过损失函数优化训练得到有标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000002
进一步地，所述迁移学习模块中，在得到有标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000003
后，将无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000004
初始化为
Figure PCTCN2022124228-appb-000005
在判别器对抗训练过程中，
Figure PCTCN2022124228-appb-000006
的参数始终冻结保持不变，
Figure PCTCN2022124228-appb-000007
的参数不断更新。
进一步地，所述迁移学习模块中，所述判别器的对抗训练包括以下步骤：
(1)有标签影像数据集S将数据
Figure PCTCN2022124228-appb-000008
输入模型
Figure PCTCN2022124228-appb-000009
得到多尺度胰腺目标影像特征
Figure PCTCN2022124228-appb-000010
将无标签影像数据集记为z～T(z)，其中z为无标签胰腺CT影像数据，无标签影像数据集T将数据
Figure PCTCN2022124228-appb-000011
输入模型
Figure PCTCN2022124228-appb-000012
得到多尺度胰腺目标影像特征
Figure PCTCN2022124228-appb-000013
k为T中的样本总数；
(2)将两组多尺度胰腺目标影像特征通过两个支路成对输入所述对抗学习模块的判别器
Figure PCTCN2022124228-appb-000014
给定有标签影像数据集S的数据
Figure PCTCN2022124228-appb-000015
标签为1，无标签影像数据集T的数据
Figure PCTCN2022124228-appb-000016
标签为0，所述判别器
Figure PCTCN2022124228-appb-000017
基于寻找有标签胰腺CT影像数据和无标签胰腺CT影像数据差异最大的权重的假设进行优化，判别器
Figure PCTCN2022124228-appb-000018
的损失函数L dis表示为：
Figure PCTCN2022124228-appb-000019
其中，
Figure PCTCN2022124228-appb-000020
为
Figure PCTCN2022124228-appb-000021
单支路输入对抗学习模块的判别器预测结果；
Figure PCTCN2022124228-appb-000022
为
Figure PCTCN2022124228-appb-000023
单支路输入对抗学习模块的判别器预测结果；更新判别器权重，得到更新后的判别器
Figure PCTCN2022124228-appb-000024
(3)判别器更新完成后，暂时保持冻结状态；将无标签影像数据集T的数据
Figure PCTCN2022124228-appb-000025
际签变化为1，将数据
Figure PCTCN2022124228-appb-000026
单支路输入当前判别器
Figure PCTCN2022124228-appb-000027
根据判别器的损失函数计算更新梯度，反向传播给无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000028
实现模型
Figure PCTCN2022124228-appb-000029
更新；
(4)重复步骤(2)、(3)，不断更新判别器
Figure PCTCN2022124228-appb-000030
和无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000031
将无标签影像数据的分割问题优化为无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000032
和判别器
Figure PCTCN2022124228-appb-000033
之间的纳什均衡，表示为：
Figure PCTCN2022124228-appb-000034
直到预设最大迭代次数完成训练，得到无标签影像数据对应的最终的胰腺影像分割模型
Figure PCTCN2022124228-appb-000035
本发明的有益效果是：
1.在有标签胰腺CT影像数据和无标签胰腺CT影像数据存在异质性时，利用对抗学习的思想，设计融合多尺度胰腺目标影像特征的判别器结构，找到有标签胰腺CT影像数据和无标签胰腺CT影像数据的共性影像特征和无标签胰腺CT影像数据的个性化影像特征之间的纳什均衡，构建适用于无标签胰腺CT影像数据的性能可靠的胰腺影像分割模型。
2.为改善胰腺组织这类小目标分割问题固有的特征语义不丰富的情况，胰腺影像分割模型设计中采用Transformer结构作为特征编码器，并设计了多阶段编码器-解码器结构，相比传统的卷积神经网络编码器，Transformer结构优点在于无需通过较深的网络层数设计让模型适应逐渐降维的特征图，而是通过对像素块分区并建立像素块之间的关系提高编码器对胰腺影像的底层特征学习能力，网络显存访问显著降低，运算速度加快；在多阶段编码器和解码器之间采用残差结构连接，对多尺度胰腺目标影像特征进行级联运算，有效解决胰腺体积小且结构复杂多变的问题。
3.本发明面向医疗中心实际应用场景，针对现有胰腺影像分割模型在影像数据存在异质性时泛化性能不佳的问题，在无需医疗人员对医疗中心的本地影像数据库中无标签胰腺CT影像数据进行标注的情况下，构建出适用于无标签胰腺CT影像的胰腺影像分割模型，实现对医疗中心的本地影像数据库中大量无标签胰腺CT影像数据的自动分割，并给出多种可靠且有指导性的可视化影像结果和结构化图表信息以展示分割结果，有效缩短医生读片时间，优化胰腺相关疾病诊疗流程，提高医生诊疗效率。
附图说明
图1为本发明实施例提供的基于对抗学习的无标签胰腺影像自动分割系统框架图；
图2为本发明实施例提供的分割模块中构建的胰腺影像分割模型结构示意图；
图3为本发明实施例提供的对抗学习模块中构建的判别器结构示意图；
图4为本发明实施例提供的迁移学习模块的工作流程图；
图5为本发明实施例提供的结果展示模块中的系统使用流程图。
具体实施方式
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图对本发明的具体实施方式做详细的说明。
本发明实施例提供一种基于对抗学习的无标签胰腺影像自动分割系统，如图1所示，该系统包括数据筛选模块、数据质量对齐模块、迁移学习模块和结果展示模块，下面详细阐述每个模块的实现过程。
一、数据筛选模块
数据筛选模块的主要任务为根据研究任务从医疗中心的本地影像数据库中筛选符合研究条件的胰腺CT影像数据，可以由系统使用者设置筛选参数，包括研究入组样本基本特征(年龄、性别、就诊时间等)、研究入组样本CT影像拍摄时间、拍摄仪器、胰腺健康状况、胰腺疾病种类等。根据设置的筛选参数从医疗中心的本地影像数据库中查询并提取胰腺CT影像数据，根据实际需求将其转换为.nii格式或.nii.gz格式用于后续分割。
二、数据质量对齐模块
数据质量对齐模块通过对胰腺CT影像数据进行影像规范化预处理，以减小不同来源胰腺CT影像间的数据异质性，对筛选出的胰腺CT影像数据从感兴趣区域筛选、超分辨率重建、数据扩增、灰度归一化等方面进行质量对齐。对于有标签胰腺CT影像数据，对其标签进行相同的规范化预处理。具体实施方式为：
感兴趣区域筛选：包括有效腹部范围框定和感兴趣层面筛选；不同来源胰腺CT影像在拍摄FOV(Field of View)上有一定差异，拍摄出的胰腺CT影像中腹部范围和视角高度不同，本发明对胰腺CT影像进行二值化处理后，对二维CT影像的图像区域属性进行度量，找到影像中所有的连通域，其中最大连通域的边界对角顶点(x 1，y 1)，(x 2，y 2)框定的矩形范围即认为是有效腹部范围；胰腺体积较小，在胰腺CT影像中只在部分层面上出现，因此利用Faster-RCNN对胰腺进行粗定位后得到有效层面范围[z start，z end]，考虑到胰腺粗定位误差和正负样本比例设置，取有效层面范围及其边界上下各G张胰腺CT影像[z start-G，z end+G]作为感兴趣层面，G根据胰腺CT影像的层厚动态设置，本实例中取G＝20。
超分辨率重建：对感兴趣区域筛选后的胰腺CT影像，在水平面上超分辨率重建至预设W*L像素尺寸，在轴向上超分辨率重建至预设层厚d，其中超分辨率重建采用三维线性立方体插值进行体素插值，超分辨率重建后的胰腺CT影像的尺寸为W*L*((z end-z start+2G)*(z 0/d))，其 中z 0为胰腺CT影像的原始层厚；本实例中，取宽W＝512，长L＝512，层厚d＝1mm。
数据扩增：针对不同胰腺CT影像在拍摄过程中角度轻微扰动带来的影像差异，对超分辨率重建后的影像进行多倍角度旋转扩增，例如可进行±5°和±10°小角度旋转扩增。
灰度归一化：腹部器官有效CT值强度范围一般为[-160，240]HU，胰腺有效CT值强度范围一般为[-100，240]HU。本发明在前述预处理步骤完成后，对当前胰腺CT影像灰度值截断至[-100，240]，并使用Min-Max归一化方法将影像灰度归一化至[0，1]。
三、迁移学习模块
迁移学习模块主要包括两个子模块：用于构建胰腺影像分割模型的分割模块、用于不同数据源间影像特征对抗学习的对抗学习模块。
所述分割模块中，胰腺影像分割模型使用多阶段编码器-解码器结构，以改善胰腺小目标分割中的特征语义不丰富的情况；编码器采用Transformer结构，多个阶段的编码器将特征层层抽象，得到多种尺度的高维细粒度胰腺目标影像特征，多尺度胰腺目标影像特征通过残差(Residual)连接，被引入对应阶段的解码器，进行目标分割特征解码计算，得到对应尺度的三维特征图；多个阶段的解码器最终输出结果表示为对输入的胰腺CT影像每一个像素点识别为前景胰腺组织或背景的预测结果，即分割掩膜。
其中，胰腺影像分割模型的输入为由原始胰腺CT影像进行像素块分区得到的所有像素块；
第一阶段编码器由线性变换操作和Swin Transformer Block组成，后续阶段编码器由像素块组合操作和Swin Transformer Block组成；线性变换操作用于将像素块转换成序列化的特征向量；像素块组合操作用于将若干相邻的像素块进行组合并降采样，之后将降采样后的像素块输入Swin Transformer Block；Swin Transformer Block由一个多头滑动窗口的自注意力模块MSA(Multi-head SelfAttention)和一个多层感知模块MLP(Multilayer Perception)组成，每个MSA和MLP前均接有LayerNorm层，并在每个MSA和MLP之后使用残差连接，Swin Transformer Block得到像素相对位置相关的特征图。每一阶段解码器都对Swin Transformer Block多层堆叠，使模型能够充分提取输入影像的特征。
解码器由上采样操作和解码模块Decoder Block组成，上采样操作由三维转置卷积(Transpose Convolution)层和激活函数层组成，Decoder Block由若干堆叠的三维卷积层和激活函数层组成。
在一个具体实例中，如图2所示，对于给定的512*512*S尺寸的原始胰腺CT影像，其中S为原始胰腺CT影像的总层数，先做p*p尺寸的像素块分区，得到
Figure PCTCN2022124228-appb-000036
数量的像素块(常用经验值包括但不限于P＝7)，所得到像素块的尺寸为P*P*S。将得到的所有像素块输入胰腺影像分割模型。胰腺影像分割模型经过4个阶段的编码器和4个阶段的解码器得到最终分割结 果。编码器1(E_Stage1)由线性变换操作和Swin Transformer Block组成，编码器2-4(E_Stage2-E_Stage4)由像素块组合操作和Swin Transformer Block组成。编码器1-4中Swin Transformer Block的数量分别为2，2，18，2。解码器1-4(DE_Stage1-DE_Stage4)由上采样操作和Decoder Block组成。其中对于编码器1，线性变换操作用于将像素块转换成序列化的特征向量，Swin Transformer Block用于得到像素相对位置相关的特征图，序列化的特征向量和Swin Transformer Block提示的像素相对位置相结合，从而得到像素块之间的位置关系。其中编码器2-4的操作一致，先通过像素块组合操作，将4个相邻的像素块以2*2规格进行组合并降采样，之后将降采样后的像素块输入Swin Transformer Block。解码器1-4由上采样操作和Decoder Block组成，上采样操作由三维转置卷积层和激活函数层PReLU组成，其中三维转置卷积步长(stride)设置为2，Decoder Block由3个堆叠的三维卷积层和激活函数层PReLU组成。编码器和解码器在同级阶段之间用残差连接，用于4种尺度胰腺目标影像特征级联交互。最后由解码器1输出分割掩膜。
所述对抗学习模块将根据有标签影像数据训练的胰腺影像分割模型作为无标签影像数据对应的初始胰腺影像分割模型，提取有标签影像数据和无标签影像数据的多尺度胰腺目标影像特征，并通过多尺度判别器对抗训练，更新无标签影像数据对应的胰腺影像分割模型。
具体地，判别器可以采用三维多尺度渐进式特征融合判别器，判别器具有多个入口，分别输入分割模块中多个阶段的解码器输出的三维特征图，本实例中判别器具有4个入口，如图3所示，分别输入来自分割模块解码器DE_Stage1-DE_Stage4输出的4种尺度的三维特征图。每张特征图依次经过三维卷积层和激活函数层(激活函数层可采用PReLU)，三维卷积层的步长可设定为2，每经过一次该操作，图像特征的空间尺度会缩小至下一个尺度的特征图大小，接着与下一个尺度的特征图进行拼接操作，一起送入下一个尺度的三维卷积层和激活函数层。多个尺度的特征图完成特征融合后，依次输入平均池化层和全连接层，输出不同数据源的预测结果，对应真实数据源标签计算判别器损失函数，并更新判别器权重。
所述迁移学习模块具体工作流程如下：
如图4所示，有标签影像数据集记为(x，y)～S((x，y))，其中x为有标签胰腺CT影像数据，y为x对应的标签。所述有标签胰腺CT影像数据可以为医疗中心诊疗产生的有胰腺精准标注的CT影像，也可以为公开发布的国内外有标注胰腺影像数据。无标签影像数据集记为z～T(z)，其中z为无标签胰腺CT影像数据。
(1)有标签影像数据集S的胰腺CT影像数据和标签数据
Figure PCTCN2022124228-appb-000037
成对输入胰腺影像分割模型，n为S中的样本总数，此时胰腺影像分割模型基于得到对于输入x i映射标签数据y i误差最小的权重的假设进行优化，将总损失函数L seg定义为交叉熵损失函数L CE和Dice Loss损失 函数L Dice的线性组合，表示为：
L seg＝L CE+λL Dice
其中，λ为线性权重系数，取模型实测经验数值，本实例中取λ＝0.5。
交叉熵损失函数L CE表示为：
Figure PCTCN2022124228-appb-000038
其中，
Figure PCTCN2022124228-appb-000039
为单个像素点预测为前景胰腺组织的概率值。
Dice Loss损失函数L Dice表示为：
Figure PCTCN2022124228-appb-000040
其中，P为胰腺影像分割模型预测的胰腺区域，Z为标签y标注的胰腺区域。
经过如上损失函数优化训练得到有标签影像数据对应的胰腺影像分割模型记为
Figure PCTCN2022124228-appb-000041
(2)将胰腺影像分割模型
Figure PCTCN2022124228-appb-000042
拷贝至无标签影像数据支路，此时无标签影像数据对应的胰腺影像分割模型初始化为
Figure PCTCN2022124228-appb-000043
在后续对抗学习过程中，有标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000044
的参数始终冻结保持不变，无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000045
的参数不断更新。
(3)有标签影像数据集S将数据
Figure PCTCN2022124228-appb-000046
输入模型
Figure PCTCN2022124228-appb-000047
得到多尺度胰腺目标影像特征
Figure PCTCN2022124228-appb-000048
无标签影像数据集T将数据
Figure PCTCN2022124228-appb-000049
输入模型
Figure PCTCN2022124228-appb-000050
得到多尺度胰腺目标影像特征
Figure PCTCN2022124228-appb-000051
k为T中的样本总数。
(4)将两组多尺度胰腺目标影像特征通过两个支路成对输入对抗学习模块的判别器
Figure PCTCN2022124228-appb-000052
给定有标签影像数据集S的数据
Figure PCTCN2022124228-appb-000053
标签为1，无标签影像数据集T的数据
Figure PCTCN2022124228-appb-000054
标签为0，判别器
Figure PCTCN2022124228-appb-000055
基于寻找有标签胰腺CT影像数据和无标签胰腺CT影像数据差异最大的权重的假设进行优化，使得判别器具有区分有标签胰腺CT影像数据和无标签胰腺CT影像数据个性化影像特征的能力，判别器
Figure PCTCN2022124228-appb-000056
的损失函数L dis表示为：
Figure PCTCN2022124228-appb-000057
其中，
Figure PCTCN2022124228-appb-000058
为
Figure PCTCN2022124228-appb-000059
单支路输入对抗学习模块的判别器预测结果；
Figure PCTCN2022124228-appb-000060
为
Figure PCTCN2022124228-appb-000061
单支路输入对抗学习模块的判别器预测结果，
Figure PCTCN2022124228-appb-000062
为求期望。
由此，判别器优化问题转换为神经网络中损失函数最小化问题，对判别器权重参数采用梯度下降法进行更新，得到更新后的判别器，记为
Figure PCTCN2022124228-appb-000063
(5)判别器更新完成后，暂时保持冻结状态。为得到有标签胰腺CT影像数据和无标签胰腺CT影像数据的共性影像特征，将无标签影像数据集T的数据
Figure PCTCN2022124228-appb-000064
示签变化为1，将数据
Figure PCTCN2022124228-appb-000065
单支路输入当前判别器
Figure PCTCN2022124228-appb-000066
中，根据判别器的损失函数计算更新梯度，反向传播给无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000067
实现模型
Figure PCTCN2022124228-appb-000068
更新。
(6)重复步骤(4)、(5)，不断更新判别器
Figure PCTCN2022124228-appb-000069
和无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000070
Figure PCTCN2022124228-appb-000071
和
Figure PCTCN2022124228-appb-000072
在交替训练时寻找有标签影像数据集S和无标签影像数据集T中数据的共性影像特征，并强化无标签影像数据集T中的个性化影像特征，即此时无标签影像数据的分割问题可优化为无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000073
和判别器
Figure PCTCN2022124228-appb-000074
之间的纳什均衡，表示为：
Figure PCTCN2022124228-appb-000075
直到预设最大迭代次数完成训练，此时得到无标签影像数据对应的最终的胰腺影像分割模型
Figure PCTCN2022124228-appb-000076
四、结果展示模块
结果展示模块对分割模块输出的分割掩膜进行后处理，并对胰腺CT影像数据及胰腺自动分割结果进行展示，展示内容分为可视化影像结果和结构化图表信息。具体实现为，如图5所示，当无标签影像数据对应的胰腺影像分割模型
Figure PCTCN2022124228-appb-000077
训练完成后，系统使用者可通过数据筛选模块选取医疗中心本地影像数据库中待研究胰腺CT影像数据，完成选定的胰腺CT影像数据的格式转换，并在数据质量对齐模块中自定义参数对胰腺CT影像数据做影像规范化预处理，利用分割模块对预处理后胰腺CT影像数据进行分割得到分割掩膜。分割过程是对胰腺CT影像中每一个像素点预测为目标前景胰腺组织或背景的概率值，因此分割掩膜中通常会存在一些孤立点和噪声点，本发明采用条件随机场(CRF)模型和空洞填充算法对分割掩膜做进一步优化以消除分割掩膜中的空洞结构并进行边缘平滑，对优化后的分割掩膜及原始胰腺CT影像提供可视化影像结果和结构化图表信息展示。可视化影像结果包括但不限于原始三维胰腺CT影像、胰腺三维分割掩膜、原始三维胰腺组织影像、胰腺二维分割掩膜、原始二维胰腺组织分层影像等，并支持鼠标拖动旋转、缩放影像等操作实现更丰富的结果展示。结构化图表信息包括但不限于胰腺体积、胰腺三维尺寸、胰腺二维分层尺寸、胰腺组织占位深度等。
以上所述仅是本发明的优选实施方式，虽然本发明已以较佳实施例披露如上，然而并非用以限定本发明。任何熟悉本领域的技术人员，在不脱离本发明技术方案范围情况下，都可利用上述揭示的方法和技术内容对本发明技术方案做出许多可能的变动和修饰，或修改为等同变化的等效实施例。因此，凡是未脱离本发明技术方案的内容，依据本发明的技术实质对以上实施例所做的任何的简单修改、等同变化及修饰，均仍属于本发明技术方案保护的范围内。

Claims (6)

  1. 一种基于对抗学习的无标签胰腺影像自动分割系统，其特征在于，包括：
    数据筛选模块：用于采集和筛选胰腺CT影像数据；
    数据质量对齐模块：用于对不同数据源的胰腺CT影像数据做影像规范化预处理；
    迁移学习模块，包括用于构建胰腺影像分割模型的分割模块、用于不同数据源间影像特征对抗学习的对抗学习模块；
    所述分割模块中构建的胰腺影像分割模型使用多阶段编码器-解码器结构，编码器和解码器在同级阶段之间用残差连接，编码器采用Transformer，多个阶段的编码器将特征层层抽象，得到多尺度胰腺目标影像特征，多尺度胰腺目标影像特征通过残差连接，被引入对应阶段的解码器，进行目标分割特征解码计算，得到对应尺度的三维特征图，多个阶段的解码器最终输出分割掩膜；
    所述胰腺影像分割模型的输入为由原始胰腺CT影像进行像素块分区得到的所有像素块；
    第一阶段编码器由线性变换操作和Swin Transformer Block组成，后续阶段编码器由像素块组合操作和Swin Transformer Block组成；所述线性变换操作用于将像素块转换成序列化的特征向量；所述像素块组合操作用于将若干相邻的像素块进行组合并降采样；所述Swin Transformer Block由一个多头滑动窗口的自注意力模块MSA和一个多层感知模块MLP组成，每个MSA和MLP前均接有LayerNorm层，并在每个MSA和MLP之后使用残差连接，所述Swin Transformer Block得到像素相对位置相关的特征图；
    解码器由上采样操作和解码模块组成，所述上采样操作由三维转置卷积层和激活函数层组成，所述解码模块由若干堆叠的三维卷积层和激活函数层组成；
    所述对抗学习模块中，将有标签影像数据训练的胰腺影像分割模型作为无标签影像数据对应的初始胰腺影像分割模型，提取有标签影像数据和无标签影像数据的多尺度胰腺目标影像特征，并通过判别器对抗训练，更新无标签影像数据对应的胰腺影像分割模型；
    所述判别器为三维多尺度渐进式特征融合判别器，所述判别器具有多个入口，分别输入所述分割模块中多个阶段的解码器输出的三维特征图，每张特征图经过降维操作后与下一个尺度的特征图进行拼接操作，所述判别器完成多尺度特征图特征融合后，输出不同数据源的预测结果，对应真实数据源标签计算判别器损失函数，并更新判别器权重；
    所述迁移学习模块中，根据有标签影像数据训练胰腺影像分割模型的过程包括：
    将有标签影像数据集记为(x，y)～S((x，y))，其中x为有标签胰腺CT影像数据，y为x对应的标签；将有标签影像数据集S的胰腺CT影像数据和标签数据
    Figure PCTCN2022124228-appb-100001
    成对输入所述分 割模块中的胰腺影像分割模型，n为S中的样本总数，所述胰腺影像分割模型基于得到对于输入x i映射标签数据y i误差最小的权重的假设进行优化，将总损失函数L seg定义为交叉熵损失函数L CE和Dice Loss损失函数L Dice的线性组合L seg＝L CE+λL Dice，λ为线性权重系数；经过损失函数优化训练得到有标签影像数据对应的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100002
    在得到有标签影像数据对应的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100003
    后，将无标签影像数据对应的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100004
    初始化为
    Figure PCTCN2022124228-appb-100005
    在判别器对抗训练过程中，
    Figure PCTCN2022124228-appb-100006
    的参数始终冻结保持不变，
    Figure PCTCN2022124228-appb-100007
    的参数不断更新；
    所述判别器的对抗训练包括以下步骤：
    (1)有标签影像数据集S将数据
    Figure PCTCN2022124228-appb-100008
    输入模型
    Figure PCTCN2022124228-appb-100009
    得到多尺度胰腺目标影像特征
    Figure PCTCN2022124228-appb-100010
    将无标签影像数据集记为z～T(z)，其中z为无标签胰腺CT影像数据，无标签影像数据集T将数据
    Figure PCTCN2022124228-appb-100011
    输入模型
    Figure PCTCN2022124228-appb-100012
    得到多尺度胰腺目标影像特征
    Figure PCTCN2022124228-appb-100013
    k为T中的样本总数；
    (2)将两组多尺度胰腺目标影像特征通过两个支路成对输入所述对抗学习模块的判别器
    Figure PCTCN2022124228-appb-100014
    给定有标签影像数据集S的数据
    Figure PCTCN2022124228-appb-100015
    标签为1，无标签影像数据集T的数据
    Figure PCTCN2022124228-appb-100016
    标签为0，所述判别器
    Figure PCTCN2022124228-appb-100017
    基于寻找有标签胰腺CT影像数据和无标签胰腺CT影像数据差异最大的权重的假设进行优化，判别器
    Figure PCTCN2022124228-appb-100018
    的损失函数L dis表示为：
    Figure PCTCN2022124228-appb-100019
    其中，
    Figure PCTCN2022124228-appb-100020
    为
    Figure PCTCN2022124228-appb-100021
    单支路输入对抗学习模块的判别器预测结果；
    Figure PCTCN2022124228-appb-100022
    为
    Figure PCTCN2022124228-appb-100023
    单支路输入对抗学习模块的判别器预测结果；更新判别器权重，得到更新后的判别器
    Figure PCTCN2022124228-appb-100024
    (3)判别器更新完成后，暂时保持冻结状态；将无标签影像数据集T的数据
    Figure PCTCN2022124228-appb-100025
    标签变化为1，将数据
    Figure PCTCN2022124228-appb-100026
    单支路输入当前判别器
    Figure PCTCN2022124228-appb-100027
    根据判别器的损失函数计算更新梯度，反向传播给无标签影像数据对应的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100028
    实现模型
    Figure PCTCN2022124228-appb-100029
    更新；
    (4)重复步骤(2)、(3)，不断更新判别器
    Figure PCTCN2022124228-appb-100030
    和无标签影像数据对应的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100031
    将无标签影像数据的分割问题优化为无标签影像数据对应的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100032
    和判别器
    Figure PCTCN2022124228-appb-100033
    之间的纳什均衡，表示为：
    Figure PCTCN2022124228-appb-100034
    直到预设最大迭代次数完成训练，得到无标签影像数据对应的最终的胰腺影像分割模型
    Figure PCTCN2022124228-appb-100035
  2. 根据权利要求1所述的一种基于对抗学习的无标签胰腺影像自动分割系统，其特征在于，所述数据质量对齐模块中，对胰腺CT影像数据进行感兴趣区域筛选，包括：
    有效腹部范围框定：对胰腺CT影像进行二值化处理，对图像区域属性进行度量，找到影像中所有连通域，最大连通域的边界对角顶点框定的矩形范围被认为是有效腹部范围；
    感兴趣层面筛选：利用目标检测算法对胰腺进行粗定位后得到有效层面范围，取有效层面范围及其边界上下若干张胰腺CT影像作为感兴趣层面。
  3. 根据权利要求2所述的一种基于对抗学习的无标签胰腺影像自动分割系统，其特征在于，所述数据质量对齐模块中，对感兴趣区域筛选后的胰腺CT影像进行超分辨率重建，在水平面上超分辨率重建至预设W*L像素尺寸，在轴向上超分辨率重建至预设层厚d，所述超分辨率重建采用三维线性立方体插值进行体素插值。
  4. 根据权利要求1所述的一种基于对抗学习的无标签胰腺影像自动分割系统，其特征在于，所述数据质量对齐模块中，对胰腺CT影像进行多倍角度旋转扩增，角度范围为[-10°,+10°]；将胰腺CT影像灰度值截断至[-100,240]，并使用Min-Max归一化方法将影像灰度归一化至[0,1]。
  5. 根据权利要求1所述的一种基于对抗学习的无标签胰腺影像自动分割系统，其特征在于，所述系统还包括结果展示模块，用于对所述分割模块输出的分割掩膜进行后处理，并提供胰腺CT影像数据及胰腺自动分割结果的可视化影像结果和结构化图表信息展示。
  6. 根据权利要求5所述的一种基于对抗学习的无标签胰腺影像自动分割系统，其特征在于，所述结果展示模块中，采用条件随机场模型和空洞填充算法对分割掩膜进行后处理；所述可视化影像结果包括原始三维胰腺CT影像、胰腺三维分割掩膜、原始三维胰腺组织影像、胰腺二维分割掩膜、原始二维胰腺组织分层影像；所述结构化图表信息包括胰腺体积、胰腺三维尺寸、胰腺二维分层尺寸、胰腺组织占位深度。
PCT/CN2022/124228 2021-12-01 2022-10-10 一种基于对抗学习的无标签胰腺影像自动分割系统 WO2023098289A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111453631.5A CN113870258B (zh) 2021-12-01 2021-12-01 一种基于对抗学习的无标签胰腺影像自动分割系统
CN202111453631.5 2021-12-01

Publications (1)

Publication Number Publication Date
WO2023098289A1 true WO2023098289A1 (zh) 2023-06-08

Family

ID=78985357

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/124228 WO2023098289A1 (zh) 2021-12-01 2022-10-10 一种基于对抗学习的无标签胰腺影像自动分割系统

Country Status (2)

Country Link
CN (1) CN113870258B (zh)
WO (1) WO2023098289A1 (zh)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116416261A (zh) * 2023-06-09 2023-07-11 南京航空航天大学 超分辨率重建辅助的ct图像超分辨率分割方法
CN116758100A (zh) * 2023-08-17 2023-09-15 神州医疗科技股份有限公司 一种3d医学图像分割系统及方法
CN116912253A (zh) * 2023-09-14 2023-10-20 吉林大学 基于多尺度混合神经网络的肺癌病理图像分类方法
CN117274883A (zh) * 2023-11-20 2023-12-22 南昌工程学院 基于多头注意力优化特征融合网络的目标跟踪方法与系统
CN117455906A (zh) * 2023-12-20 2024-01-26 东南大学 基于多尺度交叉融合和边界引导的数字化病理胰腺癌神经分割方法
CN117636076A (zh) * 2024-01-25 2024-03-01 北京航空航天大学 基于深度学习影像模型的前列腺mri图像分类方法
CN117746045A (zh) * 2024-02-08 2024-03-22 江西师范大学 一种Transformer和卷积融合的医学图像分割方法及系统
CN117809052A (zh) * 2024-03-01 2024-04-02 海豚乐智科技(成都)有限责任公司 一种块状目标检测及特征提取方法、装置及存储介质
CN117830332A (zh) * 2024-01-09 2024-04-05 四川大学 一种基于弱监督的医学图像分割方法
CN117876817A (zh) * 2023-12-25 2024-04-12 北京化工大学 一种对抗样本生成方法
CN117876370A (zh) * 2024-03-11 2024-04-12 南京信息工程大学 基于三维轴向Transformer模型的CT图像肾脏肿瘤分割系统
CN117974960A (zh) * 2024-03-28 2024-05-03 临沂大学 一种双光融合动态超分辨率分层感知方法
CN118172357A (zh) * 2024-04-15 2024-06-11 江苏省人民医院（南京医科大学第一附属医院） 基于磁共振扩散张量成像的腰骶丛神经根分割方法、介质和设备
CN118196416A (zh) * 2024-03-26 2024-06-14 昆明理工大学 一种融合多任务协同与渐进式解析策略的小目标结直肠息肉分割方法
CN118379303A (zh) * 2024-06-21 2024-07-23 华侨大学 一种基于多尺度Transformer多注意力的U-Net病理细胞分割方法及装置
CN118522427A (zh) * 2024-07-18 2024-08-20 山东工商学院 基于区块链和多模态数据的智慧医疗智能导诊方法及系统
CN118628920A (zh) * 2024-08-09 2024-09-10 自然资源部第一海洋研究所 一种基于深度学习的海洋三维温度锋面智能检测方法

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113870258B (zh) * 2021-12-01 2022-03-25 浙江大学 一种基于对抗学习的无标签胰腺影像自动分割系统
CN115953654A (zh) * 2022-03-24 2023-04-11 北京字跳网络技术有限公司 一种图像处理方法、装置、电子设备及存储介质
CN114742802B (zh) * 2022-04-19 2023-04-18 江南大学 基于3Dtransformer混合卷积神经网络的胰腺CT图像分割方法
CN114565613B (zh) * 2022-04-29 2022-08-23 之江实验室 基于有监督深度子空间学习的胰腺术后糖尿病预测系统
CN115018711B (zh) * 2022-07-15 2022-10-25 成都运荔枝科技有限公司 一种用于仓库调度的图像超分辨率重建方法
CN115101197A (zh) * 2022-07-28 2022-09-23 首都医科大学附属北京朝阳医院 胰腺癌影像学联合ca19-9诊断模型构建方法、系统及装置
CN115578406B (zh) * 2022-12-13 2023-04-07 四川大学 基于上下文融合机制的cbct颌骨区域分割方法及系统
CN116934683A (zh) * 2023-05-30 2023-10-24 中国人民解放军总医院第四医学中心 人工智能辅助超声诊断脾创伤的方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108062753A (zh) * 2017-12-29 2018-05-22 重庆理工大学 基于深度对抗学习的无监督域自适应脑肿瘤语义分割方法
US20180260957A1 (en) * 2017-03-08 2018-09-13 Siemens Healthcare Gmbh Automatic Liver Segmentation Using Adversarial Image-to-Image Network
CN112634265A (zh) * 2021-01-04 2021-04-09 西北大学 基于dnn的胰腺全自动分割模型的构建、分割方法及系统
CN113870258A (zh) * 2021-12-01 2021-12-31 浙江大学 一种基于对抗学习的无标签胰腺影像自动分割系统

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10624558B2 (en) * 2017-08-10 2020-04-21 Siemens Healthcare Gmbh Protocol independent image processing with adversarial networks
CN107742122A (zh) * 2017-10-27 2018-02-27 浙江大华技术股份有限公司 一种x光图像的分割方法及装置
CN109948798A (zh) * 2019-03-21 2019-06-28 西安电子科技大学 基于生成对抗网络模型的混合进化优化方法
CN110097131B (zh) * 2019-05-08 2023-04-28 南京大学 一种基于对抗协同训练的半监督医疗图像分割方法
CN110148142B (zh) * 2019-05-27 2023-04-18 腾讯科技（深圳）有限公司 图像分割模型的训练方法、装置、设备和存储介质
CN110363122B (zh) * 2019-07-03 2022-10-11 昆明理工大学 一种基于多层特征对齐的跨域目标检测方法
CN111583262A (zh) * 2020-04-23 2020-08-25 北京小白世纪网络科技有限公司 一种血管分割方法与系统
CN111985314B (zh) * 2020-07-09 2024-04-30 东南大学 一种基于ViBe与改进LBP的烟雾检测方法
CN112116593B (zh) * 2020-08-06 2024-06-21 北京工业大学 一种基于基尼指数的领域自适应语义分割方法
CN112270244A (zh) * 2020-10-23 2021-01-26 平安科技（深圳）有限公司 目标物违规监控方法、装置、电子设备及存储介质
CN112381831B (zh) * 2020-11-26 2022-08-16 南开大学 基于图像间语义辅助的个性化图像分割方法及系统
CN112598759B (zh) * 2020-12-15 2022-09-13 太原科技大学 抑制低剂量ct图像中伪影噪声的多尺度特征生成对抗网络
CN113408537B (zh) * 2021-07-19 2023-07-21 中南大学 一种遥感影像域适应语义分割方法
CN113674253B (zh) * 2021-08-25 2023-06-30 浙江财经大学 基于U-Transformer的直肠癌CT影像自动分割方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180260957A1 (en) * 2017-03-08 2018-09-13 Siemens Healthcare Gmbh Automatic Liver Segmentation Using Adversarial Image-to-Image Network
CN108062753A (zh) * 2017-12-29 2018-05-22 重庆理工大学 基于深度对抗学习的无监督域自适应脑肿瘤语义分割方法
CN112634265A (zh) * 2021-01-04 2021-04-09 西北大学 基于dnn的胰腺全自动分割模型的构建、分割方法及系统
CN113870258A (zh) * 2021-12-01 2021-12-31 浙江大学 一种基于对抗学习的无标签胰腺影像自动分割系统

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116416261A (zh) * 2023-06-09 2023-07-11 南京航空航天大学 超分辨率重建辅助的ct图像超分辨率分割方法
CN116416261B (zh) * 2023-06-09 2023-09-12 南京航空航天大学 超分辨率重建辅助的ct图像超分辨率分割方法
CN116758100A (zh) * 2023-08-17 2023-09-15 神州医疗科技股份有限公司 一种3d医学图像分割系统及方法
CN116912253A (zh) * 2023-09-14 2023-10-20 吉林大学 基于多尺度混合神经网络的肺癌病理图像分类方法
CN116912253B (zh) * 2023-09-14 2023-12-05 吉林大学 基于多尺度混合神经网络的肺癌病理图像分类方法
CN117274883A (zh) * 2023-11-20 2023-12-22 南昌工程学院 基于多头注意力优化特征融合网络的目标跟踪方法与系统
CN117274883B (zh) * 2023-11-20 2024-01-26 南昌工程学院 基于多头注意力优化特征融合网络的目标跟踪方法与系统
CN117455906A (zh) * 2023-12-20 2024-01-26 东南大学 基于多尺度交叉融合和边界引导的数字化病理胰腺癌神经分割方法
CN117455906B (zh) * 2023-12-20 2024-03-19 东南大学 基于多尺度交叉融合和边界引导的数字化病理胰腺癌神经分割方法
CN117876817A (zh) * 2023-12-25 2024-04-12 北京化工大学 一种对抗样本生成方法
CN117830332A (zh) * 2024-01-09 2024-04-05 四川大学 一种基于弱监督的医学图像分割方法
CN117636076A (zh) * 2024-01-25 2024-03-01 北京航空航天大学 基于深度学习影像模型的前列腺mri图像分类方法
CN117636076B (zh) * 2024-01-25 2024-04-12 北京航空航天大学 基于深度学习影像模型的前列腺mri图像分类方法
CN117746045A (zh) * 2024-02-08 2024-03-22 江西师范大学 一种Transformer和卷积融合的医学图像分割方法及系统
CN117746045B (zh) * 2024-02-08 2024-05-28 江西师范大学 一种Transformer和卷积融合的医学图像分割方法及系统
CN117809052B (zh) * 2024-03-01 2024-05-14 海豚乐智科技(成都)有限责任公司 一种块状目标检测及特征提取方法、装置及存储介质
CN117809052A (zh) * 2024-03-01 2024-04-02 海豚乐智科技(成都)有限责任公司 一种块状目标检测及特征提取方法、装置及存储介质
CN117876370A (zh) * 2024-03-11 2024-04-12 南京信息工程大学 基于三维轴向Transformer模型的CT图像肾脏肿瘤分割系统
CN117876370B (zh) * 2024-03-11 2024-06-07 南京信息工程大学 基于三维轴向Transformer模型的CT图像肾脏肿瘤分割系统
CN118196416A (zh) * 2024-03-26 2024-06-14 昆明理工大学 一种融合多任务协同与渐进式解析策略的小目标结直肠息肉分割方法
CN117974960A (zh) * 2024-03-28 2024-05-03 临沂大学 一种双光融合动态超分辨率分层感知方法
CN118172357A (zh) * 2024-04-15 2024-06-11 江苏省人民医院（南京医科大学第一附属医院） 基于磁共振扩散张量成像的腰骶丛神经根分割方法、介质和设备
CN118379303A (zh) * 2024-06-21 2024-07-23 华侨大学 一种基于多尺度Transformer多注意力的U-Net病理细胞分割方法及装置
CN118522427A (zh) * 2024-07-18 2024-08-20 山东工商学院 基于区块链和多模态数据的智慧医疗智能导诊方法及系统
CN118628920A (zh) * 2024-08-09 2024-09-10 自然资源部第一海洋研究所 一种基于深度学习的海洋三维温度锋面智能检测方法

Also Published As

Publication number Publication date
CN113870258A (zh) 2021-12-31
CN113870258B (zh) 2022-03-25

Similar Documents

Publication Publication Date Title
WO2023098289A1 (zh) 一种基于对抗学习的无标签胰腺影像自动分割系统
CN110310281B (zh) 一种基于Mask-RCNN深度学习的虚拟医疗中肺结节检测与分割方法
CN113674253B (zh) 基于U-Transformer的直肠癌CT影像自动分割方法
Zhang et al. ME‐Net: multi‐encoder net framework for brain tumor segmentation
Chen et al. HSN: hybrid segmentation network for small cell lung cancer segmentation
KR101943011B1 (ko) 피검체의 의료 영상 판독을 지원하는 방법 및 이를 이용한 장치
CN109389584A (zh) 基于cnn的多尺度鼻咽肿瘤分割方法
Sharan et al. Encoder modified U-net and feature pyramid network for multi-class segmentation of cardiac magnetic resonance images
Ma et al. HT-Net: hierarchical context-attention transformer network for medical ct image segmentation
CN112396605B (zh) 网络训练方法及装置、图像识别方法和电子设备
Tan et al. Automated vessel segmentation in lung CT and CTA images via deep neural networks
CN114202545A (zh) 一种基于UNet++的低级别胶质瘤图像分割方法
CN113781489A (zh) 一种息肉影像语义分割方法及装置
CN115908449A (zh) 一种基于改进UNet模型的2.5D医学CT影像分割方法及装置
Liang et al. Dense networks with relative location awareness for thorax disease identification
Pandey et al. Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet
Yao et al. Deepcrc: Colorectum and colorectal cancer segmentation in CT scans via deep colorectal coordinate transform
Shen et al. A cascaded fully convolutional network framework for dilated pancreatic duct segmentation
Pandey et al. A literature review on application of machine learning techniques in pancreas segmentation
Lee et al. Multi-contrast computed tomography healthy kidney atlas
Lu et al. Regional perception and multi-scale feature fusion network for cardiac segmentation
Jiang et al. A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans
Wang et al. Multi-scale boundary neural network for gastric tumor segmentation
Zhao et al. Self-Supervised Triplet Contrastive Learning for Classifying Endometrial Histopathological Images
CN115439471A (zh) 一种面向肠道部位ct影像的图像处理方法及系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22900096

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE