Explicit shape knowledge-based feature augmentation for disease progression analysis: application in liver fibrosis prediction

基于显式形状知识的特征增强用于疾病进展分析：在肝纤维化预测中的应用

• 批准号: 19K20711
• 负责人: SOUFI MAZEN
• 金额: $ 2.66万
• 依托单位: Nara Institute of Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2019
• 资助国家: 日本
• 起止时间: 2019-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-19K20711/
• 关键词: 肝線維化; 統計形状モデリング; MR画像; Liver fibrosis; Shape modeling; MR imaging; Shape analysis; Segmentation; Deep learning; Elastography

项目成果

Imaging-based Computational Biomedicine Laboratory

基于成像的计算生物医学实验室

Staging of liver fibrosis by using texture and partial least squares-based statistical shape analyses in contrast-enhanced MR images

在对比增强 MR 图像中使用纹理和基于偏最小二乘的统计形状分析对肝纤维化进行分期

• 发表时间: 2019
• 作者: Mazen Soufi;Yoshito Otake;Masatoshi Hori;Yasuharu Imai;Yoshiyuki Sawai;Takashi Ota;Noriyuki Tomiyama;Yoshinobu Sato
• 通讯作者: Yoshinobu Sato

Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis

• DOI: 10.1007/s11548-019-02084-z
• 发表时间: 2019-11-08
• 期刊: INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
• 影响因子: 3
• 作者: Soufi, Mazen;Otake, Yoshito;Sato, Yoshinobu
• 通讯作者: Sato, Yoshinobu

Evaluation of Bayesian Active Learning for Segmentation of Liver and Spleen in Large Scale Abdominal MR Data Sets

大规模腹部 MR 数据集中肝脏和脾脏分割的贝叶斯主动学习评估

• 发表时间: 2021
• 期刊: IEICE Technical Report; IEICE Tech. Rep.
• 作者: Bin Zhang;Yoshito Otake;Mazen Soufi;Masatoshi Hori;Noriyuki Tomiyama;Yoshinobu Sato
• 通讯作者: Yoshinobu Sato

Prediction of Segmentation Accuracy of Liver and Spleen in Contrast-Enhanced MR Images Using Uncertainty Estimated from Bayesian U-Net

使用贝叶斯 U-Net 估计的不确定性预测对比增强 MR 图像中肝脏和脾脏的分割精度

• 发表时间: 2020
• 作者: Bin Zhang;Mazen Soufi;Yoshito Otake;Masatoshi Hori;Noriyuki Tomiyama;Yoshinobu Sato
• 通讯作者: Yoshinobu Sato