A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

  title={A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling},
  author={Amal A. Farag and Le Lu and Holger R. Roth and Jiamin Liu and Evrim B Turkbey and Ronald M. Summers},
  journal={IEEE Transactions on Image Processing},
Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative imaging analysis, pathology detection, and surgical assistance. For organs with high anatomical variability (e.g., the pancreas), previous segmentation approaches report low accuracies, compared with well-studied organs, such as the liver or heart. We present an automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method generates a hierarchical cascade of… 

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Pancreas Segmentation in Abdominal CT Scans using Inter-/Intra-Slice Contextual Information with a Cascade Neural Network

  • Zhengzheng YangLei Zhang Yi Lv
  • Computer Science, Medicine
    2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2019
A new approach for automatic pancreas segmentation of CT images using inter-/intra-slice contextual information with a cascade neural network is proposed and outperforms the state-of-the-arts with an average Dice Similarity Coefficient of 87.72 for NIH dataset with 4-fold cross-validation.



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