Convolutional Neural Networks Based Level Set Framework for Pancreas Segmentation from CT Images

Pancreas segmentation in computed tomography (CT) is a challenging task because of the high inter-patient anatomical variability in both shape and size between patients. In this work, we proposed a convolutional neural networks based level set framework that can automatically segment a 3D images with the whole pancreas. Convolutional neural networks is applied to obtain an initial level set contour and then level set model is used to produce accurate segmentation results. Our method was compared with the state-of-the-art methods and evaluated on 20 CT images. The experiment results show that our approach achieves the highest dice scores than other methods.

[1]  Ronald M. Summers,et al.  A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling , 2015, IEEE Transactions on Image Processing.

[2]  Yan Wang,et al.  A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans , 2016, MICCAI.

[3]  Hiroshi Fujita,et al.  A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods , 2013, Comput. Math. Methods Medicine.

[4]  S. Westerlund.  Dead matter has memory , 1991 .

[5]  Chunming Li,et al.  Implicit Active Contours Driven by Local Binary Fitting Energy , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[7]  Chengwen Chu,et al.  Multi‐atlas pancreas segmentation: Atlas selection based on vessel structure , 2017, Medical Image Anal..

[8]  Ronald M. Summers,et al.  Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆ , 2017, Medical Image Anal..

[9]  Eithne Costello-Goldring.  Biology and Management of pancreatic cancer , 2007 .

[10]  Ronald M. Summers,et al.  DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation , 2015, MICCAI.

[11]  Le Lu,et al.  Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks , 2016, MICCAI.

[12]  Ronald M. Summers,et al.  Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation , 2016, MICCAI.

[13]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[14]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[16]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[17]  Yanchun Zhang,et al.  An Approach to Extraction Midsagittal Plane of Skull From Brain CT Images for Oral and Maxillofacial Surgery , 2019, IEEE Access.

[18]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.