Multi Organ Segmentation of Abdominal Organs Using Cascade Deep Learning Architecture
Keywords:
Cascaded V-Net, abdominal organ, segmentation, Deep LearningAbstract
Manual identification of the organs of the body specially the ones located in the abdominal cavity is a tedious work. The accurate segmentation of the abdominal organs is important from the clinical diagnosis and CAD support systems. Recent development in the artificial intelligence have enables us with the cutting edge techniques even for the dense semantic segmentation of medical images. This paper presents a method to automatically segment organs of the abdominal cavity using cascaded V-Net architecture. In this work, the second V-Net is trained with the output of the first stage along with the down sampled original images to provided better contextual details. The model is trained and validated using multi atlas labelling beyond the cranial vault challenge abdomen data set. F1 score of about 90% was achieved for various organs
Downloads
References
Zhou, Yuyin, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot Fishman, and Alan Yuille. "Semi-supervised 3D abdominal multi-organ segmentation via deep multi-planar co-training." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 121-140. IEEE, 2019.
Liu, Yiqiao, Madhu Gargesha, Mohammed Qutaish, Zhuxian Zhou, Bryan Scott, Hamed Yousefi, Zhengrong Lu, and David L. Wilson. "Deep learning based multi-organ segmentation and metastases segmentation in whole mouse body and the cryo-imaging cancer imaging and therapy analysis platform (CITAP)." In Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 11317, pp. 215-225. SPIE, 2020.
Gibson, Eli, Francesco Giganti, Yipeng Hu, Ester Bonmati, Steve Bandula, Kurinchi Gurusamy, Brian Davidson, Stephen P. Pereira, Matthew J. Clarkson, and Dean C. Barratt. "Automatic multi-organ segmentation on abdominal CT with dense V-networks." IEEE transactions on medical imaging 37, no. 8 (2018): 1822-1834.
Sykes, Jonathan. "Reflections on the current status of commercial automated segmentation systems in clinical practice." Journal of medical radiation sciences 61, no. 3 (2014): 131-134.
Weinstein, Adam L., and Alaa Abd-Elsayed. "Abdominal Anatomy." Pain: A Review Guide (2019): 803-805.
6.https://slu.adam.com/content.aspx?productid=117&pid=2&gid=8896
Garbar, Veronica, and Bruce W. Newton. "Anatomy, abdomen and pelvis, falciform ligament." (2019).
Konturek, S. J., P. C. Konturek, Tomasz Brzozowski, and G. A. Bubenik. "Role of melatonin in upper gastrointestinal tract." Journal of physiology and pharmacology 58, no. 6 (2007): 23-52.
Bakhti, Mostafa, Anika Böttcher, and Heiko Lickert. "Modelling the endocrine pancreas in health and disease." Nature Reviews Endocrinology 15, no. 3 (2019): 155-171.
Johansson, Malin EV, and Gunnar C. Hansson. "Immunological aspects of intestinal mucus and mucins." Nature Reviews Immunology 16, no. 10 (2016): 639-649.
Avendi, Michael R., Arash Kheradvar, and Hamid Jafarkhani. "A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI." Medical image analysis 30 (2016): 108-119.
Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." In 2016 fourth international conference on 3D vision (3DV), pp. 565-571. Ieee, 2016.
Bernard, Olivier, Johan G. Bosch, Brecht Heyde, Martino Alessandrini, Daniel Barbosa, Sorina Camarasu-Pop, Frederic Cervenansky et al. "Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography." IEEE transactions on medical imaging 35, no. 4 (2015): 967-977.
Zettinig, Oliver, Amit Shah, Christoph Hennersperger, Matthias Eiber, Christine Kroll, Hubert Kübler, Tobias Maurer et al. "Multimodal image-guided prostate fusion biopsy based on automatic deformable registration." International journal of computer assisted radiology and surgery 10 (2015): 1997-2007.
Irshad, S., Gomes, D. P., & Kim, S. T. (2023). Improved abdominal multi-organ segmentation via 3D boundary-constrained deep neural networks. IEEE Access, 11, 35097-35110.
Song, Youyi, Jeremy Yuen-Chun Teoh, Kup-Sze Choi, and Jing Qin. "Dynamic Loss Weighting for Multiorgan Segmentation in Medical Images." IEEE Transactions on Neural Networks and Learning Systems (2023).
Shen, Nanyan, Ziyan Wang, Jing Li, Huayu Gao, Wei Lu, Peng Hu, and Lanyun Feng. "Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution." Expert Systems with Applications 211 (2023): 118625.
Kaur, Harinder, Navjot Kaur, and Nirvair Neeru. "Evolution of multiorgan segmentation techniques from traditional to deep learning in abdominal CT images–A systematic review." Displays 73 (2022): 102223.
Kang, Susu, Muyuan Yang, X. Sharon Qi, Jun Jiang, and Shan Tan. "Bridging Feature Gaps to Improve Multi-Organ Segmentation on Abdominal Magnetic Resonance Image." IEEE Journal of Biomedical and Health Informatics 27, no. 3 (2022): 1477-1487.
Wu, Jiaqi, Guangxu Li, Huimin Lu, and Tohru Kamiya. "A supervoxel classification-based method for multi-organ segmentation from abdominal ct images." Journal of Image and Graphics 9, no. 1 (2021): 9-14.
Jain, Rahul, Anoushka Sutradhar, Amiya K. Dash, and Suchismita Das. "Automatic Multi-organ Segmentation on Abdominal CT scans using Deep U-Net Model." In 2021 19th OITS International Conference on Information Technology (OCIT), pp. 48-53. IEEE, 2021.
Tang, Ping, Yu-qian Zhao, and Miao Liao. "Automatic multi-organ segmentation from abdominal CT volumes with LLE-based graph partitioning and 3D Chan-Vese model." Computers in Biology and Medicine 139 (2021): 105030.
Wang, Guotai, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel et al. "Interactive medical image segmentation using deep learning with image-specific fine tuning." IEEE transactions on medical imaging 37, no. 7 (2018): 1562-1573
https://medium.com/@singole/an-extensive-guide-to-convolution-neural-network-2023-84872b16bd78
Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." In 2016 fourth international conference on 3D vision (3DV), pp. 565-571. Ieee, 2016.
Bernard, Olivier, Johan G. Bosch, Brecht Heyde, Martino Alessandrini, Daniel Barbosa, Sorina Camarasu-Pop, Frederic Cervenansky et al. "Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography." IEEE transactions on medical imaging 35, no. 4 (2015): 967-977.
Roth, H.R. et al. (2018). A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11073. Springer, Cham.
https://www.sciencedirect.com/topics/medicine-and-dentistry/hounsfield-scale
https://www.kaggle.com/code/redwankarimsony/ct-scans-dicom-files-windowing-explained
https://www.synapse.org/#!Synapse:syn3193805/wiki/217752
https://en.wikipedia.org/wiki/Hounsfield_scale
Havrilla TR, Reich NE, Haaga JR, Seidelmann FE, Cooperman AM, Alfidi RJ. Computed tomography of the gallbladder. AJR Am J Roentgenol. 1978 Jun;130(6):1059-67. doi: 10.2214/ajr.130.6.1059. PMID: 418641.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
