Computer Aided Detection for Radiologic Images

放射图像的计算机辅助检测

• 批准号: 10913204
• 负责人: Ronald M. Summers
• 金额: --
• 依托单位: CLINICAL CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10913204
• 关键词: Abdomen; Arterial Fatty Streak; Artificial Intelligence; Big Data; Body Composition; Computer Assisted; Computer-Assisted Diagnosis; Computers; Data; Detection; Diagnosis; Diagnostic; Diagnostic Errors; Disease; Early Diagnosis; Health; Image; Kidney; Kidney Calculi; Lead; Lesion; Liver; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Measurement; Medical Imaging; Methods; Muscle; Network-based; Organ; Pancreas; Patients; Performance; Physicians; Roentgen Rays; Scientific Advances and Accomplishments; Small Intestinal Carcinoid Tumor; Spleen; Techniques; Training; United States National Institutes of Health; Variant; X-Ray Computed Tomography; abdominal CT; computer aided detection; convolutional neural network; deep learning; diagnostic accuracy; improved; liver transplantation; mortality; radiological imaging; radiologist; sarcopenia; skills

The purpose of this project is to develop computer-aided diagnosis/detection (CAD) for a wide variety of radiologic images and disease types. This project uses existing NIH radiology images. We are developing techniques for segmentation of abdominal CT images to accurately locate the boundaries of the major abdominal organs such as the liver, spleen, kidneys, muscles and pancreas. We made further progress on this project, providing accurate localization and measurement of diseases such as abdominal atherosclerotic plaques. We made further progress on a project to develop computer-aided assessment of body composition on CT scans. We are developing convolutional neural networks based methods ("deep learning") on big data to train computers to detect diseases on radiology images like X-Ray, CT and MRI scans. In FY 2023, we made several scientific advances. These included (1) improved universal lesion detection from CT to multiparametric MRI potentially improving diagnosis for a wide range of diseases, (2) showing that sarcopenia used in routine pre-liver transplant abdominal CT was the only factor significantly associated with post-LT mortality, (3) improved automated detection of small bowel carcinoid tumors on CT, and (4) automated longitudinal assessment of volumetric renal stone burden on CT scans.

该项目的目的是开发用于各种放射学图像和疾病类型的计算机辅助诊断/检测（CAD）。该项目使用现有的NIH放射学图像。 我们正在开发用于分割腹部CT图像的技术，以准确定位主要腹部器官的边界，例如肝脏，脾脏，肾脏，肌肉和胰腺。我们在该项目上取得了进一步的进展，提供了诸如腹部动脉粥样硬化斑块等疾病的准确定位和测量。我们在一个项目上开发了对CT扫描中身体组成的计算机辅助评估的项目进一步进展。 我们正在针对大数据开发基于卷积神经网络的方法（“深度学习”），以训练计算机以检测X射线，CT和MRI扫描等放射学图像的疾病。 在2023财年，我们取得了一些科学进步。 These included (1) improved universal lesion detection from CT to multiparametric MRI potentially improving diagnosis for a wide range of diseases, (2) showing that sarcopenia used in routine pre-liver transplant abdominal CT was the only factor significantly associated with post-LT mortality, (3) improved automated detection of small bowel carcinoid tumors on CT, and (4) automated longitudinal assessment of CT扫描上的体积肾结石负担。

项目成果

Mesenteric vasculature-guided small bowel segmentation on 3-D CT.

• DOI: 10.1109/tmi.2013.2271487
• 发表时间: 2013-11
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Zhang W;Liu J;Yao J;Louie A;Nguyen TB;Wank S;Nowinski WL;Summers RM
• 通讯作者: Summers RM

Optimizing area under the ROC curve using semi-supervised learning.

• DOI: 10.1016/j.patcog.2014.07.025
• 发表时间: 2015-01-01
• 期刊: PATTERN RECOGNITION
• 影响因子: 8
• 作者: Wang, Shijun;Li, Diana;Petrick, Nicholas;Sahiner, Berkman;Linguraru, Marius George;Summers, Ronald M.
• 通讯作者: Summers, Ronald M.

Computer-aided detection of exophytic renal lesions on non-contrast CT images.

• DOI: 10.1016/j.media.2014.07.005
• 发表时间: 2015-01
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Liu, Jianfei;Wang, Shijun;Linguraru, Marius George;Yao, Jianhua;Summers, Ronald M.
• 通讯作者: Summers, Ronald M.

SPIE Computer-Aided Diagnosis conference anniversary review.

• DOI: 10.1117/1.jmi.9.s1.012208
• 发表时间: 2022-03
• 期刊: Journal of medical imaging (Bellingham, Wash.)

Pancreatic Tumor Growth Prediction with Multiplicative Growth and Image-Derived Motion.

通过乘法生长和图像衍生运动预测胰腺肿瘤生长。

• DOI: 10.1007/978-3-319-19992-4_39
• 发表时间: 2015
• 期刊: Information processing in medical imaging : proceedings of the ... conference
• 作者: Wong,KenCL;Summers,RonaldM;Kebebew,Electron;Yao,Jianhua
• 通讯作者: Yao,Jianhua