Li Ruikun, Chen Huai, Gong Guanzhong, Wang Lisheng
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1629-1632. doi: 10.1109/EMBC44109.2020.9176112.
Segmenting the bladder wall from MRI images is of great significance for the early detection and auxiliary diagnosis of bladder tumors. However, automatic bladder wall segmentation is challenging due to weak boundaries and diverse shapes of bladders. Level-set-based methods have been applied to this task by utilizing the shape prior of bladders. However, it is a complex operation to adjust multiple parameters manually, and to select suitable hand-crafted features. In this paper, we propose an automatic method for the task based on deep learning and anatomical constraints. First, the autoencoder is used to model anatomical and semantic information of bladder walls by extracting their low dimensional feature representations from both MRI images and label images. Then as the constraint, such priors are incorporated into the modified residual network so as to generate more plausible segmentation results. Experiments on 1092 MRI images shows that the proposed method can generate more accurate and reliable results comparing with related works, with a dice similarity coefficient (DSC) of 85.48%.
从磁共振成像（MRI）图像中分割膀胱壁对于膀胱肿瘤的早期检测和辅助诊断具有重要意义。然而，由于膀胱边界模糊和形状多样，自动膀胱壁分割具有挑战性。基于水平集的方法通过利用膀胱的形状先验已应用于该任务。然而，手动调整多个参数以及选择合适的手工特征是一项复杂的操作。在本文中，我们提出了一种基于深度学习和解剖学约束的自动方法来完成这项任务。首先，自动编码器通过从MRI图像和标签图像中提取膀胱壁的低维特征表示来对其解剖学和语义信息进行建模。然后，作为约束条件，将这些先验信息纳入改进的残差网络中，以生成更合理的分割结果。对1092幅MRI图像进行的实验表明，与相关工作相比，该方法能够生成更准确可靠的结果，骰子相似系数（DSC）为85.48%。
