Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Holger R. Roth , Le Lu , Amal Farag , Andrew Sohn , Ronald M. Summers

ABSTRACT

Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits traditional segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a holistic learning approach that integrates semantic mid-level cues of deeply-learned organ interior and boundary maps via robust spatial aggregation using random forest. Our method generates boundary preserving pixel-wise class labels for pancreas segmentation. Quantitative evaluation is performed on CT scans of 82 patients in 4-fold cross-validation. We achieve a (mean ± std. dev.) Dice Similarity Coefficient of 78.01 %±8.2 % in testing which significantly outperforms the previous state-of-the-art approach of 71.8 %±10.7 % under the same evaluation criterion. More... »

PAGES

451-459

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

ISBN

978-3-319-46722-1
978-3-319-46723-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46723-8_52

DOI

http://dx.doi.org/10.1007/978-3-319-46723-8_52

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084904832


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