Spatio-Temporal Partitioning And Description Of Full-Length Routine Fetal Anomaly Ultrasound Scans

Abstract

This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-l accuracy =0.77 and top-3 accuracy =0.94. Automated partitioning and characterisation on unlabelled full-length video scans show high correlation (ρ=0.95, p=0.0004) with workflow statistics of manually labelled videos, suggesting practicality of proposed methods.

Publication
IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

BibTex

@inproceedings{sharma_spatio-temporal_2019,
title = {Spatio-Temporal Partitioning And Description Of Full-Length Routine Fetal Anomaly Ultrasound Scans},
author = {Sharma, H. and Droste, R. and Chatelain, P. and Drukker, L. and Papageorghiou, A.T. and Noble, J.A.},
booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
doi = {10.1109/ISBI.2019.8759149},
note = {ISSN: 1945-7928},
pages = {987--990},
year = {2019}
}


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