论文标题
腹部多器官分割的边界知觉网络
Boundary-Aware Network for Abdominal Multi-Organ Segmentation
论文作者
论文摘要
自动化的腹部多器官分割是计算机辅助诊断与腹部器官相关疾病的至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了巨大的成功，但由于腹部器官的不同大小以及它们之间的含糊界限，腹部器官的准确分割仍然具有挑战性。在本文中，我们提出了一个边界知识网络（BA-NET），以在CT扫描和MRI扫描上进行腹部器官。该模型包含共享编码器，一个边界解码器和一个分割解码器。两个解码器都采用了多尺度的深度监督策略，这可以减轻可变器官大小引起的问题。边界解码器在每个量表上产生的边界概率图被用作提高分割特征图的注意。我们评估了腹部多器官分割（AMOS）挑战数据集上的BA-NET，并在CT扫描上的多器官分割中获得了89.29 $ \％$的平均骰子得分，平均骰子得分为71.92 $ \％$ \％$ \％。结果表明，BA-NET在两个分割任务上都优于NNUNET。
Automated abdominal multi-organ segmentation is a crucial yet challenging task in the computer-aided diagnosis of abdominal organ-related diseases. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of abdominal organs remains challenging, due to the varying sizes of abdominal organs and the ambiguous boundaries among them. In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable organ sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Abdominal Multi-Organ Segmentation (AMOS) Challenge dataset and achieved an average Dice score of 89.29$\%$ for multi-organ segmentation on CT scans and an average Dice score of 71.92$\%$ on MRI scans. The results demonstrate that BA-Net is superior to nnUNet on both segmentation tasks.
