TY - GEN
T1 - From large to small organ segmentation in CT using regional context
AU - Bieth, Marie
AU - Alberts, Esther
AU - Schwaiger, Markus
AU - Menze, Bjoern
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029686286&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67389-9_1
DO - 10.1007/978-3-319-67389-9_1
M3 - Conference contribution
AN - SCOPUS:85029686286
SN - 9783319673882
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 9
BT - Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
A2 - Shi, Yinghuan
A2 - Suk, Heung-Il
A2 - Suzuki, Kenji
A2 - Wang, Qian
PB - Springer Verlag
T2 - 8th 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
Y2 - 10 September 2017 through 10 September 2017
ER -