NL2037705A - Method for brain mri tumor segmentation based on cn-unet variant network - Google Patents

Method for brain mri tumor segmentation based on cn-unet variant network Download PDF

Info

Publication number
NL2037705A
NL2037705A NL2037705A NL2037705A NL2037705A NL 2037705 A NL2037705 A NL 2037705A NL 2037705 A NL2037705 A NL 2037705A NL 2037705 A NL2037705 A NL 2037705A NL 2037705 A NL2037705 A NL 2037705A
Authority
NL
Netherlands
Prior art keywords
information
layer
convolutional
feature
unet
Prior art date
Application number
NL2037705A
Other languages
English (en)
Dutch (nl)
Inventor
Zhou Dongmei
Wei Chao
Yao Hongyu
Huang Xiao
Luo Hao
Original Assignee
Univ Chengdu Technology
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 Univ Chengdu Technology filed Critical Univ Chengdu Technology
Publication of NL2037705A publication Critical patent/NL2037705A/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • 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
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • 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
    • G06N3/0475—Generative networks
    • 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
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • 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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • A61B5/0042—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part for the brain
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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]
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/032—Recognition of patterns in medical or anatomical images of protuberances, polyps nodules, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Biomedical Technology (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
NL2037705A 2024-03-19 2024-05-15 Method for brain mri tumor segmentation based on cn-unet variant network NL2037705A (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410313700.XA CN117911705B (zh) 2024-03-19 2024-03-19 一种基于GAN-UNet变体网络的脑部MRI肿瘤分割方法

Publications (1)

Publication Number Publication Date
NL2037705A true NL2037705A (en) 2024-06-14

Family

ID=90687564

Family Applications (1)

Application Number Title Priority Date Filing Date
NL2037705A NL2037705A (en) 2024-03-19 2024-05-15 Method for brain mri tumor segmentation based on cn-unet variant network

Country Status (2)

Country Link
CN (1) CN117911705B (zh)
NL (1) NL2037705A (zh)

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109685813B (zh) * 2018-12-27 2020-10-13 江西理工大学 一种自适应尺度信息的u型视网膜血管分割方法
CN111028242A (zh) * 2019-11-27 2020-04-17 中国科学院深圳先进技术研究院 一种肿瘤自动分割系统、方法及电子设备
CN111833359B (zh) * 2020-07-13 2022-07-12 中国海洋大学 基于生成对抗网络的脑瘤分割数据增强方法
CN113160234B (zh) * 2021-05-14 2021-12-14 太原理工大学 基于超分辨率和域自适应的无监督遥感图像语义分割方法
CN113298830B (zh) * 2021-06-22 2022-07-15 西南大学 一种基于自监督的急性颅内ich区域图像分割方法
EP4177828A1 (en) * 2021-11-03 2023-05-10 Tata Consultancy Services Limited Method and system for domain knowledge augmented multi-head attention based robust universal lesion detection
KR20230147492A (ko) * 2022-04-14 2023-10-23 한국교통대학교산학협력단 딥러닝 기반으로 브레인 mr 이미지에서 뇌종양 영역을 분할하는 방법 및 이를 위한 장치
CN115760586A (zh) * 2022-06-16 2023-03-07 广州大学 一种基于多尺度注意力生成对抗网络的医学图像增强方法
WO2024000161A1 (zh) * 2022-06-28 2024-01-04 中国科学院深圳先进技术研究院 Ct胰腺肿瘤自动分割方法、系统、终端以及存储介质
CN115689961A (zh) * 2022-11-03 2023-02-03 中北大学 一种用于胶质瘤spect-mri图像融合的网络模型及方法
CN116309615A (zh) * 2023-01-09 2023-06-23 西南科技大学 一种多模态mri脑肿瘤图像分割方法

Also Published As

Publication number Publication date
CN117911705B (zh) 2024-05-28
CN117911705A (zh) 2024-04-19

Similar Documents

Publication Publication Date Title
US10482603B1 (en) Medical image segmentation using an integrated edge guidance module and object segmentation network
US10430946B1 (en) Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques
Tian et al. Multi-path convolutional neural network in fundus segmentation of blood vessels
CN106408562A (zh) 基于深度学习的眼底图像视网膜血管分割方法及系统
CN112150476A (zh) 基于时空判别性特征学习的冠状动脉序列血管分割方法
CN106408001A (zh) 基于深度核哈希的感兴趣区域快速检测方法
CN112489769A (zh) 基于深度神经网络的慢性病智慧中医诊断与药物推荐系统
CN113724206B (zh) 一种基于自监督学习的眼底图像血管分割方法及系统
Xu et al. Dual-channel asymmetric convolutional neural network for an efficient retinal blood vessel segmentation in eye fundus images
WO2022127500A1 (zh) 基于多个神经网络的mri图像分割方法、装置及设备
CN113012163A (zh) 一种基于多尺度注意力网络的视网膜血管分割方法、设备及存储介质
Yang et al. Classification of diabetic retinopathy severity based on GCA attention mechanism
Luo et al. The prediction of hypertension based on convolution neural network
Yang et al. RADCU-Net: Residual attention and dual-supervision cascaded U-Net for retinal blood vessel segmentation
Zhong et al. Do you need sharpened details? Asking MMDC-Net: multi-layer multi-scale dilated convolution network for retinal vessel segmentation
Ma et al. Retinal vessel segmentation by deep residual learning with wide activation
Zhang et al. Attention-guided feature extraction and multiscale feature fusion 3d resnet for automated pulmonary nodule detection
CN113555087A (zh) 一种基于卷积神经网络算法的人工智能阅片方法
CN111784713A (zh) 一种引入注意力机制的u形心脏分割方法
Guo et al. CAFR-CNN: coarse-to-fine adaptive faster R-CNN for cross-domain joint optic disc and cup segmentation
Lin et al. Blu-gan: Bi-directional convlstm u-net with generative adversarial training for retinal vessel segmentation
Tian et al. Learning discriminative representations for fine-grained diabetic retinopathy grading
Wang et al. Optic disc detection based on fully convolutional neural network and structured matrix decomposition
CN117633558A (zh) 基于视觉语言模型的多激励融合零样本病变检测方法
Yu et al. M3U-CDVAE: Lightweight retinal vessel segmentation and refinement network