PMID- 36100342 OWN - NLM STAT- MEDLINE DCOM- 20220915 LR - 20220922 IS - 1873-2860 (Electronic) IS - 0933-3657 (Linking) VI - 131 DP - 2022 Sep TI - Deep learning with multiresolution handcrafted features for brain MRI segmentation. PG - 102365 LID - S0933-3657(22)00123-3 [pii] LID - 10.1016/j.artmed.2022.102365 [doi] AB - The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR image segmentation. The aim of this work is to propose a segmentation approach that combines multiresolution handcrafted features with CNN-based features to add directional properties and enrich the set of features to perform segmentation. The main objective is to efficiently segment the brain into three tissue classes: bone, soft tissue, and air. The proposed method combines non subsampled Contourlet (NSCT) and non subsampled Shearlet (NSST) coefficients with CNN's features using different mechanisms. The entropy value is calculated to select the most useful coefficients and reduce the input's dimensionality. The segmentation results are evaluated using fifty clinical brain MR and CT images by calculating the precision, recall, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). The results are also compared to other methods reported in the literature. The DSC of the bone class is improved from 0.6179 +/- 0.0006 to 0.6416 +/- 0.0006. The addition of multiresolution features of NSCT and NSST with CNN's features demonstrates promising results. Moreover, NSST coefficients provide more useful information than NSCT coefficients. CI - Copyright (c) 2022 Elsevier B.V. All rights reserved. FAU - Mecheter, Imene AU - Mecheter I AD - Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK. Electronic address: imene.mecheter@brunel.ac.uk. FAU - Abbod, Maysam AU - Abbod M AD - Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK. FAU - Amira, Abbes AU - Amira A AD - Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates; Institute of Artificial Intelligence, De Montfort University, Leicester, UK. FAU - Zaidi, Habib AU - Zaidi H AD - Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20220714 PL - Netherlands TA - Artif Intell Med JT - Artificial intelligence in medicine JID - 8915031 SB - IM MH - Brain/diagnostic imaging MH - *Deep Learning MH - Magnetic Resonance Imaging/methods MH - Neural Networks, Computer MH - Tomography, X-Ray Computed/methods OTO - NOTNLM OT - CNN OT - Contourlet OT - MR OT - Segmentation OT - Shearlet COIS- Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EDAT- 2022/09/14 06:00 MHDA- 2022/09/16 06:00 CRDT- 2022/09/13 21:04 PHST- 2021/11/28 00:00 [received] PHST- 2022/06/28 00:00 [revised] PHST- 2022/07/09 00:00 [accepted] PHST- 2022/09/13 21:04 [entrez] PHST- 2022/09/14 06:00 [pubmed] PHST- 2022/09/16 06:00 [medline] AID - S0933-3657(22)00123-3 [pii] AID - 10.1016/j.artmed.2022.102365 [doi] PST - ppublish SO - Artif Intell Med. 2022 Sep;131:102365. doi: 10.1016/j.artmed.2022.102365. Epub 2022 Jul 14.