مورد إلكتروني
Biomarker Localization From Deep Learning Regression Networks
| العنوان: | Biomarker Localization From Deep Learning Regression Networks |
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| المؤلفون: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial, Cano Espinosa, Carlos, González, Germán, Washko, George R., Cazorla, Miguel, San José Estépar, Raúl |
| بيانات النشر: | IEEE 2020-06 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black-box. In this work, we present a novel deep learning network structure that, when trained with only the value of the biomarker, can perform biomarker regression and the generation of an accurate localization mask simultaneously, thus enabling a qualitative assessment of the image locus that relates to the quantitative result. We showcase the proposed method with three different network structures and compare their performance against direct regression networks in four different problems: pectoralis muscle area (PMA), subcutaneous fat area (SFA), liver mass area in single slice computed tomography (CT), and Agatston score estimated from non-contrast thoracic CT images (CAC). Our results show that the proposed method improves the performance with respect to direct biomarker regression methods (correlation coefficient of 0.978, 0.998, and 0.950 for the proposed method in comparison to 0.971, 0.982, and 0.936 for the reference regression methods on PMA, SFA and CAC respectively) while achieving good localization (DICE coefficients of 0.875, 0.914 for PMA and SFA respectively, p < 0.05 for all pairs). We observe the same improvement in regression results comparing the proposed method with those obtained by quantify the outputs using an U-Net segmentation network (0.989 and 0.951 respectively). We, therefore, conclude that it is possible to obtain simultaneously good biomarker regression and localization when training biomarker regression networks using only the biomarker value. |
| مصطلحات الفهرس: | Biomarker direct regression, Biomarker localization, Coronary artery calcification, Convolutional neural networks, info:eu-repo/semantics/article |
| URL: | |
| الإتاحة: | Open access content. Open access content info:eu-repo/semantics/openAccess © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. |
| أرقام أخرى: | ESALI oai:rua.ua.es:10045/107144 IEEE Transactions on Medical Imaging. 2020, 39(6): 2121-2132. doi:10.1109/TMI.2020.2965486 0278-0062 (Print) 1558-254X (Online) 10.1109/TMI.2020.2965486 1176456475 |
| المصدر المساهم: | UNIV DE ALICANTE From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1176456475 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |