Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm
Objective: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferr...
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| Published in: | IEEE transactions on biomedical engineering Vol. 70; no. 9; pp. 1 - 12 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
IEEE
01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9294, 1558-2531, 1558-2531 |
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| Abstract | Objective: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms have similar appearances in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of each tissue layer. Methods: For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects as well as manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of <inline-formula><tex-math notation="LaTeX">0.94\pm 0.08</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">0.92\pm 0.06</tex-math></inline-formula>, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. Results: We got an average DSC of <inline-formula><tex-math notation="LaTeX">0.87\pm 0.11</tex-math></inline-formula> on the test set, which confirms the high performance of the method. Conclusion: Automatic segmentation methods can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. Significance: Timely diagnosis and treatment of BCRL are of crucial importance to prevent irreversible damage. |
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| AbstractList | Objective: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms look similar in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of tissue layers. Methods: For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects and manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of [Formula Omitted] and [Formula Omitted], respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. Results: We got an average DSC of [Formula Omitted] on the test set, which confirms the high performance of the method. Conclusion: Automatic segmentation can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. Significance: Timely diagnosis and treatment of BCRL have crucial importance in preventing irreversible damage. Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms have similar appearances in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of each tissue layer. For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects as well as manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of 0.94±0.08 and 0.92±0.06, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. We got an average DSC of 0.87±0.11 on the test set, which confirms the high performance of the method. Automatic segmentation methods can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. Timely diagnosis and treatment of BCRL are of crucial importance to prevent irreversible damage. Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms look similar in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of tissue layers.OBJECTIVEBreast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms look similar in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of tissue layers.For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects and manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of 0.94±0.08 and 0.92±0.06, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy.METHODSFor the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects and manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of 0.94±0.08 and 0.92±0.06, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy.We got an average DSC of 0.87±0.11 on the test set, which confirms the high performance of the method.RESULTSWe got an average DSC of 0.87±0.11 on the test set, which confirms the high performance of the method.Automatic segmentation can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods.CONCLUSIONAutomatic segmentation can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods.Timely diagnosis and treatment of BCRL have crucial importance in preventing irreversible damage.SIGNIFICANCETimely diagnosis and treatment of BCRL have crucial importance in preventing irreversible damage. Objective: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms have similar appearances in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of each tissue layer. Methods: For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects as well as manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of <inline-formula><tex-math notation="LaTeX">0.94\pm 0.08</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">0.92\pm 0.06</tex-math></inline-formula>, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. Results: We got an average DSC of <inline-formula><tex-math notation="LaTeX">0.87\pm 0.11</tex-math></inline-formula> on the test set, which confirms the high performance of the method. Conclusion: Automatic segmentation methods can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. Significance: Timely diagnosis and treatment of BCRL are of crucial importance to prevent irreversible damage. |
| Author | Goudarzi, Sobhan Boily, Mathieu Whyte, Jesse Kilgour, Robert D. Rivaz, Hassan Towers, Anna |
| Author_xml | – sequence: 1 givenname: Sobhan orcidid: 0000-0002-0306-8946 surname: Goudarzi fullname: Goudarzi, Sobhan organization: Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada – sequence: 2 givenname: Jesse surname: Whyte fullname: Whyte, Jesse organization: Department of Health, Kinesiology, and Applied Physiology, Concordia University, Montreal, QC, Canada – sequence: 3 givenname: Mathieu surname: Boily fullname: Boily, Mathieu organization: Department of Diagnostic Radiology, McGill University, Montreal, QC, Canada – sequence: 4 givenname: Anna surname: Towers fullname: Towers, Anna organization: Director of the Lymphedema program at the Department of Oncology, McGill University, Montreal, QC, Canada – sequence: 5 givenname: Robert D. surname: Kilgour fullname: Kilgour, Robert D. organization: Department of Health, Kinesiology, and Applied Physiology, Concordia University, Montreal, QC, Canada – sequence: 6 givenname: Hassan orcidid: 0000-0001-5800-3034 surname: Rivaz fullname: Rivaz, Hassan organization: Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37028332$$D View this record in MEDLINE/PubMed |
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| Snippet | Objective: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the... Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of... |
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| SubjectTerms | Algorithms Artificial neural networks Biomarkers Breast cancer Cancer therapies Damage prevention Datasets Deep learning Diagnosis Drainage systems Image processing Image segmentation Imaging Lymph nodes Lymphatic drainage Lymphedema Machine learning Masks Mechanical properties Medical imaging Medical ultrasound imaging Monitoring Neural networks Radio frequency Segmentation Skin Therapeutic applications Three-dimensional displays Ultrasonic imaging Ultrasound |
| Title | Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm |
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