• 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

Related outputs

Active learning using adaptable task-based prioritisation
Shaheer U. Saeed, João Ramalhinho, Mark Pinnock, Ziyi Shen, Yunguan Fu, Nina Montaña-Brown, Ester Bonmati, Dean C. Barratt, Stephen P. Pereira, Brian Davidson, Matthew J. Clarkson and Yipeng Hu 2024. Active learning using adaptable task-based prioritisation. Medical Image Analysis. 95 103181. https://doi.org/10.1016/j.media.2024.103181

Fan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes
João Ramalhinho, Thomas Dowrick, Bonmati Coll, E. and Matthew J. Clarkson 2023. Fan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes. Journal of Open Research Software. 11 (1), p. 3. https://doi.org/10.5334/jors.422

Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks
Bonmati Coll, E., Hu, Y., Grimwood, A., Johnson, G.J., Goodchild, G., Keane, M.G., Gurusamy, K., Davidson, B., Clarkson, M.J., Pereira, S.P. and Barratt, D.C. 2022. Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks. IEEE Transactions on Medical Imaging. 41 (6), pp. 1311-1319. https://doi.org/10.1109/tmi.2021.3139023

Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification
Grimwood, A., McNair, H., Hu, Y., Bonmati Coll, E., Barratt, D. and Harris, E.J. 2020. Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference. Lima, Peru 04 - 08 Oct 2020 Springer. https://doi.org/10.1007/978-3-030-59716-0_52

Novel Brain Complexity Measures Based on Information Theory
Bonmati Coll, E., Bardera, A., Feixas, M. and Boada, I. 2018. Novel Brain Complexity Measures Based on Information Theory. Entropy. 20 (7) 491. https://doi.org/10.3390/e20070491

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures
Bonmati Coll, E., Hu, Y., Gibson, E., Uribarri, L., Keane, G., Gurusami, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2018. Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures. International Journal of Computer Assisted Radiology and Surgery. 13, pp. 875-883. https://doi.org/10.1007/s11548-018-1762-2

Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks
Gibson, E., Giganti, F., Hu, Y., Bonmati Coll, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2018. Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks. IEEE Transactions on Medical Imaging. 37 (8), pp. 1822-1834. https://doi.org/10.1109/tmi.2018.2806309

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network
Bonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T. 2018. Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network. Journal of Medical Imaging. 5 (2) 021206. https://doi.org/10.1117/1.jmi.5.2.021206

Brain parcellation based on information theory
Bonmati Coll, E., Bardera, A. and Boada, I. 2017. Brain parcellation based on information theory. Computer Methods and Programs in Biomedicine. 151, pp. 203-212. https://doi.org/10.1016/j.cmpb.2017.07.012

2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions
Hu, Y., Bonmati Coll, E., Gibson, E., Hipwell, J.H., Hawkes, D.J., Bandula, S., Pereira, S.P. and Barratt, D.C. 2016. 2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Athens, Greece 17 - 21 Oct 2016 Springer. https://doi.org/10.1007/978-3-319-46720-7_60

Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound
Bonmati Coll, E., Hu, Y., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2016. Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound. Computer-Assisted and Robotic Endoscopy. CARE 2016. Athens, Greece 17 Oct 2016 Springer. https://doi.org/10.1007/978-3-319-54057-3_4

Measuring Complex Brain Networks Structure
Bonmati Ester, Bardera Anton, Boada Imma and Bonmati Coll, E. 2016. Measuring Complex Brain Networks Structure. Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics 2016. https://doi.org/10.3389/conf.fninf.2016.20.00012

Hierarchical clustering based on the information bottleneck method using a control process
Bonmati Coll, E., Bardera, A., Boada, I., Feixas, M. and Sbert, M. 2015. Hierarchical clustering based on the information bottleneck method using a control process. Pattern Analysis and Applications (PAA). 18, pp. 619-637. https://doi.org/10.1007/s10044-015-0467-1

Permalink - https://westminsterresearch.westminster.ac.uk/item/vw9z3/towards-image-guided-pancreas-and-biliary-endoscopy-automatic-multi-organ-segmentation-on-abdominal-ct-with-dense-dilated-networks


Share this

Tweet
Email

Usage statistics

66 total views
0 total downloads
These values cover views and downloads from WestminsterResearch and are for the period from September 2nd 2018, when this repository was created.