FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917566999429120 |
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| author | Alasmawi, Hussain Bricker, Leanne Yaqub, Mohammad |
| author_facet | Alasmawi, Hussain Bricker, Leanne Yaqub, Mohammad |
| contents | Ultrasound is the primary imaging modality in clinical practice during pregnancy. More than 140M fetuses are born yearly, resulting in numerous scans. The availability of a large volume of fetal ultrasound scans presents the opportunity to train robust machine learning models. However, the abundance of scans also has its challenges, as manual labeling of each image is needed for supervised methods. Labeling is typically labor-intensive and requires expertise to annotate the images accurately. This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views, reducing or eliminating the need for manual labeling. Our Fetal Ultrasound Semantic Clustering (FUSC) method is developed using a large dataset of 88,063 images and further evaluated on an additional unseen dataset of 8,187 images achieving over 92% clustering purity. The result of our investigation hold the potential to significantly impact the field of fetal ultrasound imaging and pave the way for more advanced automated labeling solutions. Finally, we make the code and the experimental setup publicly available to help advance the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_12600 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning Alasmawi, Hussain Bricker, Leanne Yaqub, Mohammad Computer Vision and Pattern Recognition Ultrasound is the primary imaging modality in clinical practice during pregnancy. More than 140M fetuses are born yearly, resulting in numerous scans. The availability of a large volume of fetal ultrasound scans presents the opportunity to train robust machine learning models. However, the abundance of scans also has its challenges, as manual labeling of each image is needed for supervised methods. Labeling is typically labor-intensive and requires expertise to annotate the images accurately. This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views, reducing or eliminating the need for manual labeling. Our Fetal Ultrasound Semantic Clustering (FUSC) method is developed using a large dataset of 88,063 images and further evaluated on an additional unseen dataset of 8,187 images achieving over 92% clustering purity. The result of our investigation hold the potential to significantly impact the field of fetal ultrasound imaging and pave the way for more advanced automated labeling solutions. Finally, we make the code and the experimental setup publicly available to help advance the field. |
| title | FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.12600 |