Deep convolutional feature details for better knee disorder diagnoses in magnetic resonance images.

Autor: Dunnhofer M; Machine Learning and Perception Lab, University of Udine, via delle Scienze 206, 33100, Udine, Italy. Electronic address: matteo.dunnhofer@uniud.it., Martinel N; Machine Learning and Perception Lab, University of Udine, via delle Scienze 206, 33100, Udine, Italy., Micheloni C; Machine Learning and Perception Lab, University of Udine, via delle Scienze 206, 33100, Udine, Italy.
Jazyk: angličtina
Zdroj: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2022 Dec; Vol. 102, pp. 102142. Date of Electronic Publication: 2022 Nov 21.
DOI: 10.1016/j.compmedimag.2022.102142
Abstrakt: Convolutional neural networks (CNNs) applied to magnetic resonance imaging (MRI) have demonstrated their ability in the automatic diagnosis of knee injuries. Despite the promising results, the currently available solutions do not take into account the particular anatomy of knee disorders. Existing works have shown that injuries are localized in small-sized knee regions near the center of MRI scans. Based on such insights, we propose MRPyrNet, a CNN architecture capable of extracting more relevant features from these regions. Our solution is composed of a Feature Pyramid Network with Pyramidal Detail Pooling, and can be plugged into any existing CNN-based diagnostic pipeline. The first module aims to enhance the CNN intermediate features to better detect the small-sized appearance of disorders, while the second one captures such kind of evidence by maintaining its detailed information. An extensive evaluation campaign is conducted to understand in-depth the potential of the proposed solution. The experimental results achieved demonstrate that the application of MRPyrNet to baseline methodologies improves their diagnostic capability, especially in the case of anterior cruciate ligament tear and meniscal tear because of MRPyrNet's ability in exploiting the relevant appearance features of such disorders. Code is available at https://github.com/matteo-dunnhofer/MRPyrNet.
Competing Interests: 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.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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