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Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation

Ouyang C, Wang S, Chen C, Li Z, Bai W, Kainz B, Rueckert D (2022)

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Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13563 LNCS

Pages Range: 59-69

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

Event location: Singapore

ISBN: 9783031167485

DOI: 10.1007/978-3-031-16749-2_6

Abstract

Probability calibration for deep models is highly desirable in safety-critical applications such as medical imaging. It makes output probabilities of deep networks interpretable, by aligning prediction probability with the actual accuracy in test data. In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable. These unreliable predictions often occur to out-of-domain (OOD) images that are caused by imaging artifacts or unseen imaging protocols. Unfortunately, most previous calibration methods for image segmentation perform sub-optimally on OOD images. To reduce the calibration error when confronted with OOD images, we propose a novel post-hoc calibration model. Our model leverages the pixel susceptibility against perturbations at the local level, and the shape prior information at the global level. The model is tested on cardiac MRI segmentation datasets that contain unseen imaging artifacts and images from an unseen imaging protocol. We demonstrate reduced calibration errors compared with the state-of-the-art calibration algorithm.

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How to cite

APA:

Ouyang, C., Wang, S., Chen, C., Li, Z., Bai, W., Kainz, B., & Rueckert, D. (2022). Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation. In Carole H. Sudre, Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Adrian Dalca, William M. Wells III, Chen Qin, Ryutaro Tanno, Koen Van Leemput, Koen Van Leemput, William M. Wells III (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 59-69). Singapore, SG: Springer Science and Business Media Deutschland GmbH.

MLA:

Ouyang, Cheng, et al. "Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation." Proceedings of the 4th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore Ed. Carole H. Sudre, Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Adrian Dalca, William M. Wells III, Chen Qin, Ryutaro Tanno, Koen Van Leemput, Koen Van Leemput, William M. Wells III, Springer Science and Business Media Deutschland GmbH, 2022. 59-69.

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