Orientation Estimation of Abdominal Ultrasound Images with Multi-Hypotheses NetworksDownload PDF

Published: 28 Feb 2022, Last Modified: 16 May 2023MIDL 2022Readers: Everyone
Keywords: ultrasound, abdominal, deep learning, orientation, multi-hypotheses, confidence, uncertainty
TL;DR: Multi-hypotheses networks are a simple and efficient alternative baseline to dropout and ensembling for uncertainty estimation.
Abstract: Ultrasound imaging can provide valuable information to clinicians during interventions, in particular when fused with other modalities. Multi-modal image registration algorithms however require a somewhat accurate initialization, which is particularly difficult to estimate for ultrasound images as their orientation is arbitrary and their content ambiguous (limited field of view, artifacts, etc.). In this work, we train neural networks to predict the absolute orientation of ultrasound frames, but also to produce a confidence for each prediction. This allows us to select only the most confident frames in the clip. Our networks are trained to produce multiple hypotheses using a simple yet overlooked meta-loss that is specifically designed to capture the ambiguity of the input data. We show on several abdominal ultrasound datasets that multi-hypotheses networks provide better uncertainty estimates than Monte-Carlo dropout while being more efficient than network ensembling. Generic, easy to implement and able to quantify both data ambiguity and out-of-distribution samples, they represent a preferable alternative to traditional baselines for uncertainty estimation. Our method produces on a clinical test estimates within $20^{\circ}$ of the true orientation, which we can use to improve the accuracy of a subsequent registration algorithm down to less than $10^{\circ}$.
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Paper Type: validation/application paper
Primary Subject Area: Uncertainty Estimation
Secondary Subject Area: Application: Other
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Code And Data: We provided the code for setting up the multi-hypotheses network in Appendix A. The datasets used are private datasets from volunteers for which we only have consent for internal use.
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