Adnexal Mass Segmentation with Ultrasound Data Synthesis

Lebbos C, Barcroft J, Tan J, Müller J, Baugh M, Vlontzos A, Saso S, Kainz B (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13565 LNCS

Pages Range: 106-116

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Singapore, SGP

ISBN: 9783031169014

DOI: 10.1007/978-3-031-16902-1_11

Abstract

Ovarian cancer is the most lethal gynaecological malignancy. The disease is most commonly asymptomatic at its early stages and its diagnosis relies on expert evaluation of transvaginal ultrasound images. Ultrasound is the first-line imaging modality for characterising adnexal masses, it requires significant expertise and its analysis is subjective and labour-intensive, therefore open to error. Hence, automating processes to facilitate and standardise the evaluation of scans is desired in clinical practice. Using supervised learning, we have demonstrated that segmentation of adnexal masses is possible, however, prevalence and label imbalance restricts the performance on under-represented classes. To mitigate this we apply a novel pathology-specific data synthesiser. We create synthetic medical images with their corresponding ground truth segmentations by using Poisson image editing to integrate less common masses into other samples. Our approach achieves the best performance across all classes, including an improvement of up to 8% when compared with nnU-Net baseline approaches.

Authors with CRIS profile

Johanna Müller Professur für Image Data Exploration and Analysis Matthew Moses George Baugh
Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Lebbos, C., Barcroft, J., Tan, J., Müller, J., Baugh, M., Vlontzos, A.,... Kainz, B. (2022). Adnexal Mass Segmentation with Ultrasound Data Synthesis. In Stephen Aylward, J. Alison Noble, Yipeng Hu, Su-Lin Lee, Zachary Baum, Zhe Min (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 106-116). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.

MLA:

Lebbos, Clara, et al. "Adnexal Mass Segmentation with Ultrasound Data Synthesis." Proceedings of the 3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Stephen Aylward, J. Alison Noble, Yipeng Hu, Su-Lin Lee, Zachary Baum, Zhe Min, Springer Science and Business Media Deutschland GmbH, 2022. 106-116.

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