000			02133 am a22002413u 4500
042			_adc
100	1	0	_aByrne, Nick _eauthor _92491
700	1	0	_aClough, James R. _eauthor _92492
700	1	0	_aValverde, Israel _eauthor _92493
700	1	0	_aMontana, Giovanni _eauthor _92494
700	1	0	_aKing, Andrew P. _eauthor _92495
245	0	0	_aA persistent homology-based topological loss for CNN-based multi-class segmentation of CMR
260			_c2022-08-31.
500			_a/pmc/articles/PMC7614102/
500			_a/pubmed/36044487
520			_aMulti-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. Therefore, whilst sharing a high spatial overlap with the ground truth, inferred CNN-based segmentations can lack coherence, including spurious connected components, holes and voids. Such results are implausible, violating anticipated anatomical topology. In response, (single-class) persistent homology-based loss functions have been proposed to capture global anatomical features. Our work extends these approaches to the task of multi-class segmentation. Building an enriched topological description of all class labels and class label pairs, our loss functions make predictable and statistically significant improvements in segmentation topology using a CNN-based postprocessing framework. We also present (and make available) a highly efficient implementation based on cubical complexes and parallel execution, enabling practical application within high resolution 3D data for the first time. We demonstrate our approach on 2D short axis and 3D whole heart CMR segmentation, advancing a detailed and faithful analysis of performance on two publicly available datasets.
540			_a
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nIEEE Trans Med Imaging
856	4	1	_uhttp://dx.doi.org/10.1109/TMI.2022.3203309 _zConnect to this object online.
999			_c925 _d925