Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands
To diagnose Gougerot–Sjögren syndrome (GSS), ultrasound imaging (US) is a promising tool for helping physicians and experts. Our project focuses on the automatic detection of the presence of GSS using US. Ultrasound imaging suffers from a weak signal-to-noise ratio. Therefore, any classification or...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/11/1283 |
| _version_ | 1797460131600924672 |
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| author | Aurélien Olivier Clément Hoffmann Sandrine Jousse-Joulin Ali Mansour Luc Bressollette Benoit Clement |
| author_facet | Aurélien Olivier Clément Hoffmann Sandrine Jousse-Joulin Ali Mansour Luc Bressollette Benoit Clement |
| author_sort | Aurélien Olivier |
| collection | DOAJ |
| description | To diagnose Gougerot–Sjögren syndrome (GSS), ultrasound imaging (US) is a promising tool for helping physicians and experts. Our project focuses on the automatic detection of the presence of GSS using US. Ultrasound imaging suffers from a weak signal-to-noise ratio. Therefore, any classification or segmentation task based on these images becomes a difficult challenge. To address these two tasks, we evaluate different approaches: a classification using a machine learning method along with feature extraction based on a set of measurements following the radiomics guidance and a deep-learning-based classification. We propose, therefore, an innovative method to enhance the training of a deep neural network with a two phases: multiple supervision using joint classification and a segmentation implemented as pretraining. We highlight the fact that our learning methods provide segmentation results similar to those performed by human experts. We obtain proficient segmentation results for salivary glands and promising detection results for Gougerot–Sjögren syndrome; we observe maximal accuracy with the model trained in two phases. Our experimental results corroborate the fact that deep learning and radiomics combined with ultrasound imaging can be a promising tool for the above-mentioned problems. |
| first_indexed | 2024-03-09T17:00:42Z |
| format | Article |
| id | doaj.art-81a7d163ed20427f8ed1986f78b96dc2 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T17:00:42Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-81a7d163ed20427f8ed1986f78b96dc22023-11-24T14:29:48ZengMDPI AGBioengineering2306-53542023-11-011011128310.3390/bioengineering10111283Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary GlandsAurélien Olivier0Clément Hoffmann1Sandrine Jousse-Joulin2Ali Mansour3Luc Bressollette4Benoit Clement5ENSTA Bretagne, Lab-STICC UMR CNRS 6285, 29200 Brest, FranceGETBO UMR 13-04 CHRU Cavale Blanche, 29200 Brest, FranceGETBO UMR 13-04 CHRU Cavale Blanche, 29200 Brest, FranceENSTA Bretagne, Lab-STICC UMR CNRS 6285, 29200 Brest, FranceGETBO UMR 13-04 CHRU Cavale Blanche, 29200 Brest, FranceENSTA Bretagne, Lab-STICC UMR CNRS 6285, 29200 Brest, FranceTo diagnose Gougerot–Sjögren syndrome (GSS), ultrasound imaging (US) is a promising tool for helping physicians and experts. Our project focuses on the automatic detection of the presence of GSS using US. Ultrasound imaging suffers from a weak signal-to-noise ratio. Therefore, any classification or segmentation task based on these images becomes a difficult challenge. To address these two tasks, we evaluate different approaches: a classification using a machine learning method along with feature extraction based on a set of measurements following the radiomics guidance and a deep-learning-based classification. We propose, therefore, an innovative method to enhance the training of a deep neural network with a two phases: multiple supervision using joint classification and a segmentation implemented as pretraining. We highlight the fact that our learning methods provide segmentation results similar to those performed by human experts. We obtain proficient segmentation results for salivary glands and promising detection results for Gougerot–Sjögren syndrome; we observe maximal accuracy with the model trained in two phases. Our experimental results corroborate the fact that deep learning and radiomics combined with ultrasound imaging can be a promising tool for the above-mentioned problems.https://www.mdpi.com/2306-5354/10/11/1283machine learningdeep learningtexture analysisradiomicsclassificationmulti-supervision |
| spellingShingle | Aurélien Olivier Clément Hoffmann Sandrine Jousse-Joulin Ali Mansour Luc Bressollette Benoit Clement Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands Bioengineering machine learning deep learning texture analysis radiomics classification multi-supervision |
| title | Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands |
| title_full | Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands |
| title_fullStr | Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands |
| title_full_unstemmed | Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands |
| title_short | Machine and Deep Learning Approaches Applied to Classify Gougerot–Sjögren Syndrome and Jointly Segment Salivary Glands |
| title_sort | machine and deep learning approaches applied to classify gougerot sjogren syndrome and jointly segment salivary glands |
| topic | machine learning deep learning texture analysis radiomics classification multi-supervision |
| url | https://www.mdpi.com/2306-5354/10/11/1283 |
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