• Conference paper

Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks

Gibson, E., Giganti, F., Hu, Y., Bonmati Coll, E., Bandula, S., Gurusamy, K., Davidson, B.R., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2017. Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Quebec City, QC, Canada 11 - 13 Sep 2017 Springer. https://doi.org/10.1007/978-3-319-66182-7_83

TitleTowards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks
AuthorsGibson, E., Giganti, F., Hu, Y., Bonmati Coll, E., Bandula, S., Gurusamy, K., Davidson, B.R., Pereira, S.P., Clarkson, M.J. and Barratt, D.C.
TypeConference paper
Abstract

Segmentation of anatomy on abdominal CT enables patient-specific image guidance in clinical endoscopic procedures and in endoscopy training. Because robust interpatient registration of abdominal images is necessary for existing multi-atlas- and statistical-shape-model-based segmentations, but remains challenging, there is a need for automated multi-organ segmentation that does not rely on registration. We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior. The algorithm was evaluated with an 8-fold cross-validation and compared to a joint-label-fusion-based segmentation based on Dice scores and boundary distances. The proposed algorithm yielded more accurate segmentations than the joint-label-fusion-based algorithm for the pancreas (median Dice scores 66 vs 37), stomach (83 vs 72) and esophagus (73 vs 54) and marginally less accurate segmentation for the liver (92 vs 93). We conclude that dilated convolutional networks with dense skip connections can segment the liver, pancreas, stomach and esophagus from abdominal CT without image registration and have the potential to support image-guided navigation in gastrointestinal endoscopy procedures.

Year2017
ConferenceMedical Image Computing and Computer Assisted Intervention − MICCAI 2017
PublisherSpringer
Publication dates
Published04 Sep 2017
JournalLecture Notes in Computer Science
Journal citation10433, pp. 728-736
ISSN0302-9743
ISBN9783319661810
9783319661827
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-66182-7_83

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