607
IRUS TotalDownloads
Altmetric
Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation
File | Description | Size | Format | |
---|---|---|---|---|
SUPPLEMENTARY.pdf | Supporting information | 73.72 kB | Adobe PDF | View/Open |
08051114.pdf | Published version | 8.9 MB | Adobe PDF | View/Open |
Title: | Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation |
Authors: | Oktay, O Ferrante, E Kamnitsas, K Heinrich, M Bai, W Caballero, J Cook, S De Marvao, A Dawes, T O'Regan, D Kainz, B Glocker, B Rueckert, D |
Item Type: | Journal Article |
Abstract: | Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies. |
Issue Date: | 1-Feb-2018 |
Date of Acceptance: | 14-Aug-2017 |
URI: | http://hdl.handle.net/10044/1/50440 |
DOI: | 10.1109/TMI.2017.2743464 |
ISSN: | 0278-0062 |
Publisher: | IEEE |
Start Page: | 384 |
End Page: | 395 |
Journal / Book Title: | IEEE Transactions on Medical Imaging |
Volume: | 37 |
Issue: | 2 |
Copyright Statement: | © 2017 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/. |
Sponsor/Funder: | Engineering & Physical Science Research Council (EPSRC) British Heart Foundation Imperial College Healthcare NHS Trust- BRC Funding |
Funder's Grant Number: | EP/P001009/1 PG/12/27/29489 RDC04 |
Keywords: | Science & Technology Technology Life Sciences & Biomedicine Computer Science, Interdisciplinary Applications Engineering, Biomedical Engineering, Electrical & Electronic Imaging Science & Photographic Technology Radiology, Nuclear Medicine & Medical Imaging Computer Science Engineering Shape prior convolutional neural network medical image segmentation image super-resolution AUTOMATIC SEGMENTATION SHAPE MODELS VENTRICLE Algorithms Cardiac Imaging Techniques Cardiomyopathies Databases, Factual Heart Humans Imaging, Three-Dimensional Magnetic Resonance Imaging Neural Networks, Computer Heart Humans Cardiomyopathies Imaging, Three-Dimensional Magnetic Resonance Imaging Algorithms Databases, Factual Cardiac Imaging Techniques Neural Networks, Computer cs.CV cs.CV Nuclear Medicine & Medical Imaging 08 Information and Computing Sciences 09 Engineering |
Publication Status: | Published |
Online Publication Date: | 2017-09-26 |
Appears in Collections: | Computing Institute of Clinical Sciences Faculty of Medicine Department of Brain Sciences Faculty of Engineering |