Abstract
One of the foremost challenges of using Deep Neural Network-based methods for a fully automated segmentation in clinics is the lack of performance guarantee. In the foreseeable future, a feasible and promising way is that radiologists sign off the machine’s segmentation results and make corrections if needed. As a result, the human effort for image segmentation that we try to minimize will be dominated by segmentation correction. While such effort can be reduced by the advance of segmentation models, for ultrasound a novel direction can be explored: optimizing the data acquisition. We observe a substantial variation of segmentation quality among repetitive scans of the same subject even if they all have high visual quality. Based on this observation, we propose a framework to help sonographers obtain ultrasound videos that not only meet the existing quality standard but also result in better segmentation results. The promising result demonstrates the feasibility of optimizing the data acquisition for efficient human-machine collaboration.
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Ding, Y. et al. (2021). Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_44
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DOI: https://doi.org/10.1007/978-3-030-87193-2_44
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