eprintid: 10044219 rev_number: 37 eprint_status: archive userid: 608 dir: disk0/10/04/42/19 datestamp: 2018-02-27 14:35:48 lastmod: 2021-09-20 22:15:40 status_changed: 2018-10-22 08:16:19 type: article metadata_visibility: show creators_name: Gibson, E creators_name: Giganti, F creators_name: Hu, Y creators_name: Bonmati, E creators_name: Bandula, S creators_name: Gurusamy, K creators_name: Davidson, B creators_name: Pereira, SP creators_name: Clarkson, MJ creators_name: Barratt, DC title: Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks ispublished: pub divisions: UCL divisions: B02 divisions: C10 divisions: D17 divisions: G91 divisions: D16 divisions: G85 divisions: B04 divisions: C05 divisions: F42 keywords: Abdominal CT, Segmentation, Deep learning, Pancreas, Gastrointestinal tract, Stomach, Duodenum, Esophagus, Liver, Spleen, Kidney, Gallbladder note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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 deeplearning- 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, 0.74 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. date: 2018-02-14 date_type: published official_url: https://doi.org/10.1109/TMI.2018.2806309 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1539596 doi: 10.1109/TMI.2018.2806309 lyricists_name: Bandula, Steve lyricists_name: Barratt, Dean lyricists_name: Bonmati Coll, Ester lyricists_name: Clarkson, Matthew lyricists_name: Davidson, Brian lyricists_name: Gibson, Eli lyricists_name: Giganti, Francesco lyricists_name: Gurusamy, Kurinchi lyricists_name: Hu, Yipeng lyricists_name: Pereira, Stephen lyricists_id: SBAND86 lyricists_id: DBARR55 lyricists_id: EBONM94 lyricists_id: MJCLA42 lyricists_id: BRDAV24 lyricists_id: EGIBS90 lyricists_id: FGIGA51 lyricists_id: GURUS55 lyricists_id: YHUXX66 lyricists_id: SPPER57 actors_name: Gibson, Eli actors_id: EGIBS90 actors_role: owner full_text_status: public publication: IEEE Transactions on Medical Imaging volume: 37 number: 8 pagerange: 1822-1834 issn: 0278-0062 citation: Gibson, E; Giganti, F; Hu, Y; Bonmati, E; Bandula, S; Gurusamy, K; Davidson, B; ... Barratt, DC; + view all <#> Gibson, E; Giganti, F; Hu, Y; Bonmati, E; Bandula, S; Gurusamy, K; Davidson, B; Pereira, SP; Clarkson, MJ; Barratt, DC; - view fewer <#> (2018) Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks. IEEE Transactions on Medical Imaging , 37 (8) pp. 1822-1834. 10.1109/TMI.2018.2806309 <https://doi.org/10.1109/TMI.2018.2806309>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10044219/1/TMI2806309.pdf