@inbook{6c7037753e304db799c0d763ac3d96c8,
title = "Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps",
abstract = "Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single {\textquoteleft}4 Chamber Heart{\textquoteright} view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).",
keywords = "Atlas, Segmentation, Ultrasound",
author = "Samuel Budd and Matthew Sinclair and Thomas Day and Athanasios Vlontzos and Jeremy Tan and Tianrui Liu and Jacqueline Matthew and Emily Skelton and John Simpson and Reza Razavi and Ben Glocker and Daniel Rueckert and Robinson, {Emma C.} and Bernhard Kainz",
note = "Funding Information: Acknowledgements. We thank the volunteers and sonographers at St. Thomas{\textquoteright} Hospital London. The work of E.C.R. was supported by the Academy of Medical Sciences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy{\textquoteright}s and St Thomas{\textquoteright} NHS Foundation Trust and King{\textquoteright}s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy{\textquoteright}s and St Thomas{\textquoteright}, and Nvidia GPU donations. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2021",
doi = "10.1007/978-3-030-87234-2_20",
language = "English",
isbn = "9783030872335",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "207--217",
editor = "{de Bruijne}, Marleen and Cattin, {Philippe C.} and St{\'e}phane Cotin and Nicolas Padoy and Stefanie Speidel and Yefeng Zheng and Caroline Essert",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings",
address = "Germany",
}