eprintid: 1561200 rev_number: 26 eprint_status: archive userid: 608 dir: disk0/01/56/12/00 datestamp: 2017-12-14 12:16:57 lastmod: 2021-11-23 00:54:53 status_changed: 2017-12-14 12:16:57 type: proceedings_section metadata_visibility: show creators_name: Iglesias, JE title: Globally Optimal Coupled Surfaces for Semi-automatic Segmentation of Medical Images ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F42 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Manual delineations are of paramount importance in medical imaging, for instance to train supervised methods and evaluate automatic segmentation algorithms. In volumetric images, manually tracing regions of interest is an excruciating process in which much time is wasted labeling neighboring 2D slices that are similar to each other. Here we present a method to compute a set of discrete minimal surfaces whose boundaries are specified by user-provided segmentations on one or more planes. Using this method, the user can for example manually delineate one slice every n and let the algorithm complete the segmentation for the slices in between. Using a discrete framework, this method globally minimizes a cost function that combines a regularizer with a data term based on image intensities, while ensuring that the surfaces do not intersect each other or leave holes in between. While the resulting optimization problem is an integer program and thus NP-hard, we show that the equality constraint matrix is totally unimodular, which enables us to solve the linear program (LP) relaxation instead. We can then capitalize on the existence of efficient LP solvers to compute a globally optimal solution in practical times. Experiments on two different datasets illustrate the superiority of the proposed method over the use of independent, label-wise optimal surfaces (∼ 5% mean increase in Dice when one every six slices is labeled, with some structures improving up to ∼ 10% in Dice). date: 2017-05-23 date_type: published publisher: Springer official_url: http://dx.doi.org/10.1007/978-3-319-59050-9_48 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1300473 doi: 10.1007/978-3-319-59050-9_48 lyricists_name: Iglesias Gonzalez, Juan lyricists_id: JEIGL66 actors_name: Allington-Smith, Dominic actors_id: DAALL44 actors_role: owner full_text_status: public series: Lecture Notes in Computer Science publication: IPMI 2017: Information Processing in Medical Imaging volume: 10265 place_of_pub: Cham, Switzerland pagerange: 610-621 event_title: International Conference on Information Processing in Medical Imaging event_location: Boone, NC, USA event_dates: 25 June 2017 - 30 June 2017 institution: International Conference on Information Processing in Medical Imaging book_title: Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings editors_name: Niethammer, M editors_name: Styner, M editors_name: Aylward, S editors_name: Zhu, H editors_name: Oguz, I editors_name: Yap, PT editors_name: Shen, D citation: Iglesias, JE; (2017) Globally Optimal Coupled Surfaces for Semi-automatic Segmentation of Medical Images. In: Niethammer, M and Styner, M and Aylward, S and Zhu, H and Oguz, I and Yap, PT and Shen, D, (eds.) Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings. (pp. pp. 610-621). Springer: Cham, Switzerland. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1561200/1/Paper_23_Iglesias.pdf