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Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

BACKGROUND: To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness. METHODS: The sample selection involved 40 healthy right knee volumes from...

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Autores principales: Kulseng, Carl Petter Skaar, Nainamalai, Varatharajan, Grøvik, Endre, Geitung, Jonn-Terje, Årøen, Asbjørn, Gjesdal, Kjell-Inge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847207/
https://www.ncbi.nlm.nih.gov/pubmed/36650496
http://dx.doi.org/10.1186/s12891-023-06153-y
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author Kulseng, Carl Petter Skaar
Nainamalai, Varatharajan
Grøvik, Endre
Geitung, Jonn-Terje
Årøen, Asbjørn
Gjesdal, Kjell-Inge
author_facet Kulseng, Carl Petter Skaar
Nainamalai, Varatharajan
Grøvik, Endre
Geitung, Jonn-Terje
Årøen, Asbjørn
Gjesdal, Kjell-Inge
author_sort Kulseng, Carl Petter Skaar
collection PubMed
description BACKGROUND: To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness. METHODS: The sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The Dice similarity coefficient (DSC), Jaccard index and Hausdorff were used to compare the performance of the networks. RESULTS: Combining all sequences collectively performed significantly better than other alternatives. The following DSCs (±standard deviation) were obtained for the test dataset: Bone medulla 0.997 (±0.002), PCL 0.973 (±0.015), ACL 0.964 (±0.022), muscle 0.998 (±0.001), cartilage 0.966 (±0.018), bone cortex 0.980 (±0.010), arteries 0.943 (±0.038), collateral ligaments 0.919 (± 0.069), tendons 0.982 (±0.005), meniscus 0.955 (±0.032), adipose tissue 0.998 (±0.001), veins 0.980 (±0.010) and nerves 0.921 (±0.071). The deep learning network correctly identified the anterior cruciate ligament (ACL) tear of the left knee, thus indicating a future aid to orthopaedics. CONCLUSIONS: The convolutional neural network proves highly capable of correctly labeling all anatomical structures of the knee joint when applied to 3D MR sequences. We have demonstrated that this deep learning model is capable of automatized segmentation that may give 3D models and discover pathology. Both useful for a preoperative evaluation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-023-06153-y.
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spelling pubmed-98472072023-01-19 Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol Kulseng, Carl Petter Skaar Nainamalai, Varatharajan Grøvik, Endre Geitung, Jonn-Terje Årøen, Asbjørn Gjesdal, Kjell-Inge BMC Musculoskelet Disord Research BACKGROUND: To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness. METHODS: The sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The Dice similarity coefficient (DSC), Jaccard index and Hausdorff were used to compare the performance of the networks. RESULTS: Combining all sequences collectively performed significantly better than other alternatives. The following DSCs (±standard deviation) were obtained for the test dataset: Bone medulla 0.997 (±0.002), PCL 0.973 (±0.015), ACL 0.964 (±0.022), muscle 0.998 (±0.001), cartilage 0.966 (±0.018), bone cortex 0.980 (±0.010), arteries 0.943 (±0.038), collateral ligaments 0.919 (± 0.069), tendons 0.982 (±0.005), meniscus 0.955 (±0.032), adipose tissue 0.998 (±0.001), veins 0.980 (±0.010) and nerves 0.921 (±0.071). The deep learning network correctly identified the anterior cruciate ligament (ACL) tear of the left knee, thus indicating a future aid to orthopaedics. CONCLUSIONS: The convolutional neural network proves highly capable of correctly labeling all anatomical structures of the knee joint when applied to 3D MR sequences. We have demonstrated that this deep learning model is capable of automatized segmentation that may give 3D models and discover pathology. Both useful for a preoperative evaluation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-023-06153-y. BioMed Central 2023-01-18 /pmc/articles/PMC9847207/ /pubmed/36650496 http://dx.doi.org/10.1186/s12891-023-06153-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Kulseng, Carl Petter Skaar
Nainamalai, Varatharajan
Grøvik, Endre
Geitung, Jonn-Terje
Årøen, Asbjørn
Gjesdal, Kjell-Inge
Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol
title Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol
title_full Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol
title_fullStr Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol
title_full_unstemmed Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol
title_short Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol
title_sort automatic segmentation of human knee anatomy by a convolutional neural network applying a 3d mri protocol
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847207/
https://www.ncbi.nlm.nih.gov/pubmed/36650496
http://dx.doi.org/10.1186/s12891-023-06153-y
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