Development and Evaluation of Computerized Segmentation Algorithm for 3D Multimodality Breast Images

TL;DRAbstract

Breast cancer is the 2nd most common cancer among US women. Until now, mammography still has been a widely accepted screening tool for breast cancer. However, mammography projects 3D tissue structures of the breast onto a 2D plane and results in superimposition effect which leads to misdiagnoses.\n\nRecently, researchers have been developing CT systems (bCT) and automated 3D breast ultrasound (ABUS) dedicated solely for breast imaging. Such imaging modalities generate 3D image volumes that completely resolve breast tissue structures and avoid the superimposition effect. However, it also produces large amount of image data that the radiologists need to review. Such data explosion could make image interpretation task even more difficult and time consuming. Therefore, CAD (computer-aided detection/diagnosis) technology is expected to alleviate the burden. \n\nIn this study, a technique for CAD application to bCT and ABUS is developed. We aim to propose an automated segmentation procedure

Chat with Paper

AI Agents for this Paper