Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challengi...
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| Published in | IEEE transactions on medical imaging Vol. 37; no. 8; pp. 1822 - 1834 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0278-0062 1558-254X 1558-254X |
| DOI | 10.1109/TMI.2018.2806309 |
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| Abstract | Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures. |
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| AbstractList | Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the GI tract (esophagus, stomach, duodenum) and surrounding organs (liver, spleen, left kidney, gallbladder). We directly compared the segmentation accuracy of the proposed method to existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 vs. 0.71, and 0.74 for the pancreas, 0.90 vs 0.85, 0.87 and 0.83 for the stomach and 0.76 vs 0.68, 0.69 and 0.66 for the esophagus. We conclude that deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures. Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures. Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures. |
| Author | Bonmati, Ester Barratt, Dean C. Giganti, Francesco Gibson, Eli Clarkson, Matthew J. Hu, Yipeng Pereira, Stephen P. Davidson, Brian Bandula, Steve Gurusamy, Kurinchi |
| AuthorAffiliation | Department of Radiology, University College Hospital Trust, UK Institute for Liver and Digestive Health, University College London, UK Division of Surgery and Interventional Science, University College London, UK UCL Centre for Medical Imaging, University College London, UK Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, UK UCL Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, UK |
| AuthorAffiliation_xml | – name: Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, UK – name: UCL Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, UK – name: UCL Centre for Medical Imaging, University College London, UK – name: Division of Surgery and Interventional Science, University College London, UK – name: Institute for Liver and Digestive Health, University College London, UK – name: Department of Radiology, University College Hospital Trust, UK |
| Author_xml | – sequence: 1 givenname: Eli orcidid: 0000-0001-9207-7280 surname: Gibson fullname: Gibson, Eli email: eli.gibson@ucl.ac.uk organization: Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, University College London, London, U.K – sequence: 2 givenname: Francesco orcidid: 0000-0001-5218-6431 surname: Giganti fullname: Giganti, Francesco organization: Department of Radiology, University College Hospital Trust, London, U.K – sequence: 3 givenname: Yipeng orcidid: 0000-0003-4902-0486 surname: Hu fullname: Hu, Yipeng organization: Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, University College London, London, U.K – sequence: 4 givenname: Ester orcidid: 0000-0001-9217-5438 surname: Bonmati fullname: Bonmati, Ester organization: Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, University College London, London, U.K – sequence: 5 givenname: Steve orcidid: 0000-0002-4558-288X surname: Bandula fullname: Bandula, Steve organization: UCL Centre for Medical Imaging, University College London, London, U.K – sequence: 6 givenname: Kurinchi orcidid: 0000-0002-0313-9134 surname: Gurusamy fullname: Gurusamy, Kurinchi organization: Division of Surgery and Interventional Science, University College London, London, U.K – sequence: 7 givenname: Brian orcidid: 0000-0002-9152-5907 surname: Davidson fullname: Davidson, Brian organization: Division of Surgery and Interventional Science, University College London, London, U.K – sequence: 8 givenname: Stephen P. orcidid: 0000-0003-0821-1809 surname: Pereira fullname: Pereira, Stephen P. organization: Institute for Liver and Digestive Health, University College London, London, U.K – sequence: 9 givenname: Matthew J. orcidid: 0000-0002-5565-1252 surname: Clarkson fullname: Clarkson, Matthew J. organization: Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, University College London, London, U.K – sequence: 10 givenname: Dean C. orcidid: 0000-0003-2916-655X surname: Barratt fullname: Barratt, Dean C. organization: Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, University College London, London, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29994628$$D View this record in MEDLINE/PubMed |
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| CODEN | ITMID4 |
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| Snippet | Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows.... Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning and treatment delivery workflows.... |
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| SubjectTerms | Abdomen Abdominal CT Accuracy Algorithms Computed tomography Deep learning Digestive System - diagnostic imaging Duodenum Endoscopy Esophagus Gallbladder Gastrointestinal system Gastrointestinal tract Humans Image processing Image segmentation Kidney Kidney - diagnostic imaging Kidneys Liver Machine learning Mathematical models Medical imaging Methods Organs Pancreas Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Abdominal - methods Registration segmentation Spleen Spleen - diagnostic imaging Statistical analysis Statistical methods Statistical models Stomach Three-dimensional displays Tomography, X-Ray Computed - methods |
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| Title | Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks |
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