Computer Aided Detection for Radiologic Images

放射图像的计算机辅助检测

• 批准号: 10683666
• 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/10683666
• 关键词: Abdomen; Arterial Fatty Streak; Artificial Intelligence; Big Data; Body Composition; Cancer Patient; Computer Assisted; Computer-Assisted Diagnosis; Computers; Control Groups; Data; Detection; Diagnosis; Diagnostic; Diagnostic Errors; Disease; Health; Image; Kidney; Lead; Lesion; Liver; Lymph; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant neoplasm of prostate; Measurement; Medical Imaging; Methods; Muscle; Network-based; Organ; Pancreas; Patients; Performance; Physicians; Roentgen Rays; Scientific Advances and Accomplishments; Small Intestines; 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; radiological imaging; radiologist; skills training; theories

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 2022, we made several scientific advances. These included (1) improved universal lesion detection on CT potentially improving diagnosis for a wide range of diseases, (2) showing that abdominal aortic and iliac atherosclerotic plaque burden in prostate cancer patients did not differ from a control group disproving a theory about an association, (3) improved automated abnormal lymph detection on abdominal MRI, and (4) improved methods to segment and track the small intestine on abdominal 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2022财年，我们取得了一些科学进步。 These included (1) improved universal lesion detection on CT potentially improving diagnosis for a wide range of diseases, (2) showing that abdominal aortic and iliac atherosclerotic plaque burden in prostate cancer patients did not differ from a control group disproving a theory about an association, (3) improved automated abnormal lymph detection on abdominal MRI, and (4) improved methods to segment and track the small腹部CT扫描上的肠。