CN114511564B - 基于dce-mri对乳腺癌残存肿瘤负荷的影像分析方法 - Google Patents
基于dce-mri对乳腺癌残存肿瘤负荷的影像分析方法 Download PDFInfo
- Publication number
- CN114511564B CN114511564B CN202210408166.1A CN202210408166A CN114511564B CN 114511564 B CN114511564 B CN 114511564B CN 202210408166 A CN202210408166 A CN 202210408166A CN 114511564 B CN114511564 B CN 114511564B
- Authority
- CN
- China
- Prior art keywords
- image
- mri
- breast cancer
- omics
- dce
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
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
- G06F18/214—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
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- 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
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30068—Mammography; Breast
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Quality & Reliability (AREA)
- Computational Linguistics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Probability & Statistics with Applications (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
- Image Processing (AREA)
Abstract
Description
Claims (5)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210408166.1A CN114511564B (zh) | 2022-04-19 | 2022-04-19 | 基于dce-mri对乳腺癌残存肿瘤负荷的影像分析方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210408166.1A CN114511564B (zh) | 2022-04-19 | 2022-04-19 | 基于dce-mri对乳腺癌残存肿瘤负荷的影像分析方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114511564A CN114511564A (zh) | 2022-05-17 |
CN114511564B true CN114511564B (zh) | 2023-01-24 |
Family
ID=81554953
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210408166.1A Active CN114511564B (zh) | 2022-04-19 | 2022-04-19 | 基于dce-mri对乳腺癌残存肿瘤负荷的影像分析方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114511564B (zh) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116030261A (zh) * | 2023-03-29 | 2023-04-28 | 浙江省肿瘤医院 | Mri影像多组学评估乳腺癌同源重组修复缺陷的方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112309576A (zh) * | 2020-09-22 | 2021-02-02 | 江南大学 | 基于深度学习ct影像组学的结直肠癌生存期预测方法 |
CN113208640A (zh) * | 2021-04-26 | 2021-08-06 | 复旦大学附属肿瘤医院 | 一种基于乳腺专用pet影像组学预测腋窝淋巴结转移的方法 |
CN113643269A (zh) * | 2021-08-24 | 2021-11-12 | 泰安市中心医院 | 基于无监督学习的乳腺癌分子分型方法、装置及系统 |
CN114267434A (zh) * | 2021-12-27 | 2022-04-01 | 西南医科大学附属医院 | 一种基于影像组学的癌患者肿瘤图像勾画方法 |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111094977B (zh) * | 2017-07-13 | 2024-02-13 | 古斯塔夫·鲁西研究所 | 监测抗pd-1/pd-l1治疗的肿瘤患者中肿瘤淋巴细胞浸润和预后的基于影像组学的成像工具 |
CN108898160B (zh) * | 2018-06-01 | 2022-04-08 | 中国人民解放军战略支援部队信息工程大学 | 基于cnn和影像组学特征融合的乳腺癌组织病理学分级方法 |
CN109086572A (zh) * | 2018-07-24 | 2018-12-25 | 南方医科大学南方医院 | 一种用于评估胃癌术后预后和化疗反应性的试剂和方法 |
CN111382756B (zh) * | 2018-12-28 | 2023-06-02 | 台湾中国医药大学附设医院 | 影像电脑辅助直肠癌治疗反应预测系统及方法 |
US11810292B2 (en) * | 2019-09-30 | 2023-11-07 | Case Western Reserve University | Disease characterization and response estimation through spatially-invoked radiomics and deep learning fusion |
US11896349B2 (en) * | 2019-12-09 | 2024-02-13 | Case Western Reserve University | Tumor characterization and outcome prediction through quantitative measurements of tumor-associated vasculature |
CN111370128A (zh) * | 2020-03-05 | 2020-07-03 | 上海市肺科医院(上海市职业病防治院) | 一种肺癌患者预后预测系统及方法 |
CN111739033A (zh) * | 2020-06-22 | 2020-10-02 | 中山大学肿瘤防治中心(中山大学附属肿瘤医院、中山大学肿瘤研究所) | 基于机器学习的乳腺钼靶及mr图像影像组学模型的建立方法 |
-
2022
- 2022-04-19 CN CN202210408166.1A patent/CN114511564B/zh active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112309576A (zh) * | 2020-09-22 | 2021-02-02 | 江南大学 | 基于深度学习ct影像组学的结直肠癌生存期预测方法 |
CN113208640A (zh) * | 2021-04-26 | 2021-08-06 | 复旦大学附属肿瘤医院 | 一种基于乳腺专用pet影像组学预测腋窝淋巴结转移的方法 |
CN113643269A (zh) * | 2021-08-24 | 2021-11-12 | 泰安市中心医院 | 基于无监督学习的乳腺癌分子分型方法、装置及系统 |
CN114267434A (zh) * | 2021-12-27 | 2022-04-01 | 西南医科大学附属医院 | 一种基于影像组学的癌患者肿瘤图像勾画方法 |
Also Published As
Publication number | Publication date |
---|---|
CN114511564A (zh) | 2022-05-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110934606B (zh) | 脑卒中早期平扫ct图像评估系统及评估方法、可读存储介质 | |
CN106056595B (zh) | 基于深度卷积神经网络自动识别甲状腺结节良恶性的辅助诊断系统 | |
CN108288070B (zh) | 一种神经指纹提取分类方法及系统 | |
KR102108050B1 (ko) | 증강 컨볼루션 네트워크를 통한 유방암 조직학 이미지 분류 방법 및 그 장치 | |
US20170249739A1 (en) | Computer analysis of mammograms | |
CN111539930A (zh) | 基于深度学习的动态超声乳腺结节实时分割与识别的方法 | |
CN108257135A (zh) | 基于深度学习方法解读医学图像特征的辅助诊断系统 | |
WO2013088144A1 (en) | Probability mapping for visualisation and analysis of biomedical images | |
Eyal et al. | Model‐based and model‐free parametric analysis of breast dynamic‐contrast‐enhanced MRI | |
CN111340770B (zh) | 结合全局加权lbp和纹理分析的癌症预后模型构建方法 | |
He et al. | Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography | |
Nandihal et al. | Glioma Detection using Improved Artificial Neural Network in MRI Images | |
CN112419320B (zh) | 基于sam与多层uda的跨模态心脏分割方法 | |
CN114693933A (zh) | 基于生成对抗网络和多尺度特征融合的医学影像分割装置 | |
CN112767407A (zh) | 一种基于级联门控3DUnet模型的CT图像肾脏肿瘤分割方法 | |
Yang et al. | Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images | |
Alksas et al. | A novel computer-aided diagnostic system for early assessment of hepatocellular carcinoma | |
CN114511564B (zh) | 基于dce-mri对乳腺癌残存肿瘤负荷的影像分析方法 | |
CN115496720A (zh) | 基于ViT机制模型的胃肠癌病理图像分割方法及相关设备 | |
CN112638262B (zh) | 相似度确定装置、方法及程序 | |
EP3074949A2 (en) | Method and system for determining the prognosis of a patient suffering from pulmonary embolism | |
Gloger et al. | Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences | |
Çetingül et al. | Estimation of local orientations in fibrous structures with applications to the Purkinje system | |
US20220375077A1 (en) | Method for generating models to automatically classify medical or veterinary images derived from original images into at least one class of interest | |
CN113902738A (zh) | 一种心脏mri分割方法及系统 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information |
Inventor after: Shao Zhenzhen Inventor after: Li Yanbo Inventor after: Lu Hong Inventor after: Zhu Ying Inventor after: Ji Yu Inventor after: Xu Yilin Inventor before: Lu Hong Inventor before: Shao Zhenzhen Inventor before: Li Yanbo Inventor before: Zhu Ying Inventor before: Ji Yu Inventor before: Xu Yilin |
|
CB03 | Change of inventor or designer information | ||
GR01 | Patent grant | ||
GR01 | Patent grant |