From large to small organ segmentation in CT using regional context

Marie Bieth, Esther Alberts, Markus Schwaiger, Bjoern Menze

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The segmentation of larger organs in CT is a well studied problem. For lungs and liver, state of the art methods reach Dice Scores above 0.9. However, these methods are not as reliable on smaller organs such as pancreas, thyroid, adrenal glands and gallbladder, even though a good segmentation of these organs is needed for example for radiotherapy planning. In this work, we present a new method for the segmentation of such small organs that does not require any deformable registration to be performed. We encode regional context in the form of anatomical context and shape features. These are used within an iterative procedure where, after an initial labelling of all organs using local context only, the segmentation of small organs is refined using regional context. Finally, the segmentations are regularised by shape voting. On the Visceral Challenge 2015 dataset, our method yields a substantially higher sensitivity and Dice score than other forest-based methods for all organs. By using only affine registrations, it is also computationally highly efficient.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
EditorsYinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang
PublisherSpringer Verlag
Pages1-9
Number of pages9
ISBN (Print)9783319673882
DOIs
StatePublished - 2017
Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 10 Sep 201710 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period10/09/1710/09/17

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