Abstract
Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, make the segmentation task challenging. To alleviate these challenges, in this article, we propose a cascaded multitask 3-D fully convolution network (FCN) to automatically segment the pancreas. Our cascaded network is composed of two parts. The first part focuses on fast locating the region of the pancreas, and the second part uses a multitask FCN with dense connections to refine the segmentation map for fine voxel-wise segmentation. In particular, our multitask FCN with dense connections is implemented to simultaneously complete tasks of the voxel-wise segmentation and skeleton extraction from the pancreas. These two tasks are complementary, that is, the extracted skeleton provides rich information about the shape and size of the pancreas in retroperitoneum, which can boost the segmentation of pancreas. The multitask FCN is also designed to share the low- and mid-level features across the tasks. A feature consistency module is further introduced to enhance the connection and fusion of different levels of feature maps. Evaluations on two pancreas datasets demonstrate the robustness of our proposed method in correctly segmenting the pancreas in various settings. Our experimental results outperform both baseline and state-of-the-art methods. Moreover, the ablation study shows that our proposed parts/modules are critical for effective multitask learning.
Highlights
P ANCREATIC cancer, like ductal adenocarcinoma, has a high mortality rate with a low five-year survival rate, and is one of the most challenging cancers to treat [3]
A multitask fully convolution network (FCN) with dense connections is utilized in the second stage of the cascaded framework, denoted as C2, to accurately segment pancreas based on the detected region(s) from C1
This multitask FCN consists of two interrelated steps, that is, pancreas skeleton extraction and segmentation
Summary
P ANCREATIC cancer, like ductal adenocarcinoma, has a high mortality rate with a low five-year survival rate, and is one of the most challenging cancers to treat [3]. Yu et al [8] added a recurrent saliency transformation module into the coarse-to-fine model, which achieved the best performance among all existing methods in terms of dice ratio All of these methods merge contexts of different views of 2-D slices of CT images for segmentation, which unavoidably miss some spatial information across slices. We argue that to segment the detailed and fine structures, like pancreas, shape-specific cues can significantly improve the segmentation performance To this end, in this article, we propose a cascaded 3-D FCN, composed of two major cascaded stages (see Fig. 2 for an overview of our approach). The second stage employs a 3-D multitask dense-U-Net architecture to perform accurate segmentation on the located pancreas region With this two-stage method, small pancreas can be accurately segmented. Our proposed 3-D segmentation framework can leverage rich spatial information along all three axes for accurate segmentation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
