Comparative Analysis of Convolutional Neural Network Architectures for Automated Knee Segmentation in Medical Imaging: A Performance EvaluationSource: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 005::page 51006-1DOI: 10.1115/1.4064450Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, which is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumors, for knee joints, in particular, to segment the femoral bone from magnetic resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as the Dice similarity coefficient and Hausdorff distance. In the training phase, various combinations of hyperparameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice similarity coefficients of 0.94 and Hausdorff distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios.
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contributor author | Ghidotti, Anna | |
contributor author | Vitali, Andrea | |
contributor author | Regazzoni, Daniele | |
contributor author | Cohen, Miri Weiss | |
contributor author | Rizzi, Caterina | |
date accessioned | 2024-04-24T22:33:02Z | |
date available | 2024-04-24T22:33:02Z | |
date copyright | 1/29/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 1530-9827 | |
identifier other | jcise_24_5_051006.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295429 | |
description abstract | Segmentation of anatomical components is a major step in creating accurate and realistic 3D models of the human body, which are used in many clinical applications, including orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, which is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumors, for knee joints, in particular, to segment the femoral bone from magnetic resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as the Dice similarity coefficient and Hausdorff distance. In the training phase, various combinations of hyperparameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice similarity coefficients of 0.94 and Hausdorff distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Comparative Analysis of Convolutional Neural Network Architectures for Automated Knee Segmentation in Medical Imaging: A Performance Evaluation | |
type | Journal Paper | |
journal volume | 24 | |
journal issue | 5 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4064450 | |
journal fristpage | 51006-1 | |
journal lastpage | 51006-9 | |
page | 9 | |
tree | Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 005 | |
contenttype | Fulltext |