A Multi-task Approach Using Positional Information for Ultrasound Placenta Segmentation
Zimmer VA, Gomez A, Skelton E, Ghavami N, Wright R, Li L, Matthew J, Hajnal JV, Schnabel JA (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12437 LNCS
Pages Range: 264-273
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030603335
DOI: 10.1007/978-3-030-60334-2_26
Abstract
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to its high variations in shape, position and appearance. Convolutional neural networks (CNN) are the state-of-the-art in medical image segmentation and have already been applied successfully to extract the placenta in US. However, the performance of CNNs depends highly on the availability of large training sets which also need to be representative for new unseen data. In this work, we propose to inform the network about the variability in the data distribution via an auxiliary task to improve performances for under representative training sets. The auxiliary task has two objectives: (i) enlarging of the training set with easily obtainable labels, and (ii) including more information about the variability of the data in the training process. In particular, we use transfer learning and multi-task learning to incorporate the placental position in a U-Net architecture. We test different models for the segmentation of anterior and posterior placentas in fetal US. Our results suggest that these placenta types represent different distributions. By including the position of the placenta as an auxiliary task, the segmentation accuracy for both anterior and posterior placentas is improved when the specific type of placenta is not included in the training set.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Zimmer, V.A., Gomez, A., Skelton, E., Ghavami, N., Wright, R., Li, L.,... Schnabel, J.A. (2020). A Multi-task Approach Using Positional Information for Ultrasound Placenta Segmentation. In Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 264-273). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Zimmer, Veronika A., et al. "A Multi-task Approach Using Positional Information for Ultrasound Placenta Segmentation." Proceedings of the 1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena, Springer Science and Business Media Deutschland GmbH, 2020. 264-273.
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