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However, the existing models do not adapt to possible object anomalies due to the presence of a disease or a surgical procedure. Post\u2010processing operation does not solve the problem, for example, tissue classification to detect and remove such anomalies inside the resulting segmentation mask, because segmentation errors on healthy tissues cannot be fixed. Such anomalies very often alter the shape and texture of the brain structures, making them different from the appearance of the model. In this paper, we present an effective and efficient adaptive probabilistic atlas, named AdaPro, to circumvent the problem and evaluate it on a challenging task \u2014 the segmentation of the left hemisphere, right hemisphere, and cerebellum, without pons and medulla, in 3D MR\u2010T1 brain images of Epilepsy patients. This task is challenging due to temporal lobe resections, artifacts, and the absence of contrast in some parts between the structures of interest.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In AdaPro, we first build one probabilistic atlas per object of interest from a training set with normal 3D images and the corresponding 3D object masks. Second, we incorporate a texture classifier based on convex optimization which <jats:italic>dynamically<\/jats:italic> indicates the regions of the target 3D image where the PAs (shape constraints) should be further adapted. This strategy is mathematically more elegant and avoids problems with post\u2010processing. Third, we add a new object\u2010based delineation algorithm based on combinatorial optimization and diffusion filtering. AdaPro can then be used to locate and delineate the objects in the coordinate space of the atlas or of the test image. We also compare AdaPro with three other state\u2010of\u2010the\u2010art methods: an statistical shape model based on synergistic object search and delineation, and two methods based on multi\u2010atlas label fusion (MALF).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We evaluate the methods quantitatively on 3D MR\u2010T1 brain images of 2T and 3T from epilepsy patients, before and after temporal lobe resections, and on the <jats:italic>template<\/jats:italic> and <jats:italic>native<\/jats:italic> coordinate spaces. The results show that AdaPro is considerably faster and consistently more accurate than the baselines with statistical significance in both coordinate spaces.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>AdaPro can be used as a fast and effective step for brain tissue segmentation and it can also be easily extended to segment subcortical brain structures. By choice of its components, probabilistic atlas, texture classifier, and delineation algorithm, it can also be extended to other organs and imaging modalities.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13771","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T01:09:31Z","timestamp":1566176971000},"page":"4940-4950","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An adaptive probabilistic atlas for anomalous brain segmentation in MR images"],"prefix":"10.1002","volume":"46","author":[{"given":"Samuel Botter","family":"Martins","sequence":"first","affiliation":[{"name":"Laboratory of Image Data Science (LIDS) Institute of Computing University of Campinas Campinas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jord\u00e3o","family":"Bragantini","sequence":"additional","affiliation":[{"name":"Laboratory of Image Data Science (LIDS) Institute of Computing University of Campinas Campinas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre Xavier","family":"Falc\u00e3o","sequence":"additional","affiliation":[{"name":"Laboratory of Image Data Science (LIDS) Institute of Computing University of Campinas Campinas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clarissa Lin","family":"Yasuda","sequence":"additional","affiliation":[{"name":"School of Medical Sciences University of Campinas Campinas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,9,11]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.668699"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2013.05.002"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.08.014"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.01.021"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.06.012"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.650882"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.460030304"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.bioeng.2.1.315"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1999.0815"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4938577"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.824224"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1261076"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2015.02.005"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2016.00030"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.143"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.828354"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.02.018"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75757-3_64"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4867855"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2035616"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.07.002"},{"key":"e_1_2_7_25_1","doi-asserted-by":"crossref","unstructured":"ArtaechevarriaX Mu\u00f1oz\u2010BarrutiaA Ortiz\u2010de Sol\u00f3rzanoC.Efficient classifier generation and weighted voting for atlas\u2010based segmentation: two small steps faster and closer to the combination oracle. 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