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Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs
With the increased reliance on medical imaging, Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipelines. However, training accurate and reliable classification models often require large fine-grained annotated datasets....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365288/ https://www.ncbi.nlm.nih.gov/pubmed/37492689 http://dx.doi.org/10.3389/fradi.2022.1061402 |
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author | Rajapaksa, Sajith Khalvati, Farzad |
author_facet | Rajapaksa, Sajith Khalvati, Farzad |
author_sort | Rajapaksa, Sajith |
collection | PubMed |
description | With the increased reliance on medical imaging, Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipelines. However, training accurate and reliable classification models often require large fine-grained annotated datasets. To alleviate this, weakly-supervised methods can be used to obtain local information such as region of interest from global labels. This work proposes a weakly-supervised pipeline to extract Relevance Maps of medical images from pre-trained 3D classification models using localized perturbations. The extracted Relevance Map describes a given region’s importance to the classification model and produces the segmentation for the region. Furthermore, we propose a novel optimal perturbation generation method that exploits 3D superpixels to find the most relevant area for a given classification using U-net architecture. This model is trained with perturbation loss, which maximizes the difference between unperturbed and perturbed predictions. We validated the effectiveness of our methodology by applying it to the segmentation of Glioma brain tumours in MRI scans using only classification labels for glioma type. The proposed method outperforms existing methods in both Dice Similarity Coefficient for segmentation and resolution for visualizations. |
format | Online Article Text |
id | pubmed-10365288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103652882023-07-25 Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs Rajapaksa, Sajith Khalvati, Farzad Front Radiol Radiology With the increased reliance on medical imaging, Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipelines. However, training accurate and reliable classification models often require large fine-grained annotated datasets. To alleviate this, weakly-supervised methods can be used to obtain local information such as region of interest from global labels. This work proposes a weakly-supervised pipeline to extract Relevance Maps of medical images from pre-trained 3D classification models using localized perturbations. The extracted Relevance Map describes a given region’s importance to the classification model and produces the segmentation for the region. Furthermore, we propose a novel optimal perturbation generation method that exploits 3D superpixels to find the most relevant area for a given classification using U-net architecture. This model is trained with perturbation loss, which maximizes the difference between unperturbed and perturbed predictions. We validated the effectiveness of our methodology by applying it to the segmentation of Glioma brain tumours in MRI scans using only classification labels for glioma type. The proposed method outperforms existing methods in both Dice Similarity Coefficient for segmentation and resolution for visualizations. Frontiers Media S.A. 2022-12-21 /pmc/articles/PMC10365288/ /pubmed/37492689 http://dx.doi.org/10.3389/fradi.2022.1061402 Text en © 2022 Rajapaksa and Khalvati. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Radiology Rajapaksa, Sajith Khalvati, Farzad Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs |
title | Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs |
title_full | Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs |
title_fullStr | Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs |
title_full_unstemmed | Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs |
title_short | Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs |
title_sort | relevance maps: a weakly supervised segmentation method for 3d brain tumours in mris |
topic | Radiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365288/ https://www.ncbi.nlm.nih.gov/pubmed/37492689 http://dx.doi.org/10.3389/fradi.2022.1061402 |
work_keys_str_mv | AT rajapaksasajith relevancemapsaweaklysupervisedsegmentationmethodfor3dbraintumoursinmris AT khalvatifarzad relevancemapsaweaklysupervisedsegmentationmethodfor3dbraintumoursinmris |