2026-04-17T06:08:17Zhttps://recercat.cat/oai/request
oai:recercat.cat:10256/172152024-05-22T09:50:06Zcom_2072_452955com_2072_2054col_2072_452957
RECERCAT author Kushibar, Kaisar author Valverde Valverde, Sergi author González Villà, Sandra author Bernal Moyano, Jose author Cabezas Grebol, Mariano author Oliver i Malagelada, Arnau author Lladó Bardera, Xavier 2024-05-22T09:50:06Z 2024-05-22T09:50:06Z 2019-05-01 http://hdl.handle.net/10256/17215 In recent years, some convolutional neural networks (CNNs) have been proposed to segment sub-cortical brain structures from magnetic resonance images (MRIs). Although these methods provide accurate segmentation, there is a reproducibility issue regarding segmenting MRI volumes from different image domains – e.g., differences in protocol, scanner, and intensity profile. Thus, the network must be retrained from scratch to perform similarly in different imaging domains, limiting the applicability of such methods in clinical settings. In this paper, we employ the transfer learning strategy to solve the domain shift problem. We reduced the number of training images by leveraging the knowledge obtained by a pretrained network, and improved the training speed by reducing the number of trainable parameters of the CNN. We tested our method on two publicly available datasets – MICCAI 2012 and IBSR – and compared them with a commonly used approach: FIRST. Our method showed similar results to those obtained by a fully trained CNN, and our method used a remarkably smaller number of images from the target domain. Moreover, training the network with only one image from MICCAI 2012 and three images from IBSR datasets was sufficient to significantly outperform FIRST with (p < 0.001) and (p < 0.05), respectivelyKaisar Kushibar and Jose Bernal hold FI-DGR2017 grant from the Catalan Government with reference numbers 2017FI_B00372 and 2017FI_B00476, respectively. This work has been partially supported by La Fundació la Marató de TV3, by Retos de Investigación TIN2014-55710-R, TIN2015-73563-JIN, and DPI2017-86696-R from the Ministerio de Ciencia y Tecnologia Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Imatges -- Processament Image processing Cervell -- Imatgeria per ressonància magnètica Brain -- Magnetic resonance imaging Imatges -- Segmentació Imaging segmentation Imatgeria mèdica Imaging systems in medicine Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed