上海科技大学知识管理系统

Prostate segmentation from magnetic resonance (MR) images plays an important role in prostate cancer diagnosis and treatment. Previous works typically overlooked large variations of prostate shapes, especially on the boundary area. Furthermore, the small glandular areas at ending slices also make the task very challenging. To overcome these problems, this paper presents a two-stage framework that explicitly utilizes prostate morphological representations (e.g., point, boundary) to accurately localize the prostate region with a coarse volumetric segmentation. Based on the 3D coarse outputs of the first stage, a 2D segmentation network with multi-slice association is further introduced to produce more reliable and accurate segmentation, due to large slice thickness in prostate MR images. Besides, several novel loss functions are further designed to enhance the consistency of prostate boundaries. Extensive experiments on large prostate MRI dataset show superior performance of our proposed method compared to several state-of-the-art methods. © 2021, Springer Nature Switzerland AG.