FUSQA: Fetal Ultrasound Segmentation Quality Assessment
Document Type
Conference Proceeding
Publication Title
Proceedings of Machine Learning Research
Abstract
Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness for clinical adoption. Normally, a transition to new unseen data requires time-consuming and costly quality assurance processes to validate the segmentation performance post-transition. Segmentation quality assessment efforts have focused on natural images, where the problem has been typically formulated as a dice score regression task. In this paper, we propose a simplified Fetal Ultrasound Segmentation Quality Assessment (FUSQA) model to tackle the segmentation quality assessment when no masks exist to compare with. We formulate the segmentation quality assessment process as an automated classification task to distinguish between good and poor quality segmentation masks for more accurate gestational age estimation. We validate the performance of our proposed approach on two datasets we collect from two hospitals using different ultrasound machines. We compare different architectures, with our best-performing architecture achieving over 90% classification accuracy on distinguishing between good and poor quality segmentation masks from an unseen dataset. Additionally, there was only a 1.45-days difference between the gestational age reported by doctors and estimated based on CRL measurements using well-segmented masks. On the other hand, this difference increased and reached up to 7.73 days when we calculated CRL from the poorly segmented masks. As a result, AI-based approaches can potentially aid fetal ultrasound segmentation quality assessment and might detect poor segmentation in real-time screening in the future.
First Page
1666
Last Page
1679
Publication Date
1-1-2023
Keywords
deep learning, fetal ultrasound, gestational age estimation, Segmentation quality assessment
Recommended Citation
S. Cengiz et al., "FUSQA: Fetal Ultrasound Segmentation Quality Assessment," Proceedings of Machine Learning Research, vol. 227, pp. 1666 - 1679, Jan 2023.