Sparse Structured Prediction for Semantic Edge Detection in Medical Images

Lasse Hansen, Mattias P. Heinrich
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:250-259, 2019.

Abstract

In medical image analysis most state-of-the-art methods rely on deep neural networks with learned convolutional filters. For pixel-level tasks, e.g. multi-class segmentation, approaches build upon UNet-like encoder-decoder architectures show impressive results. However, at the same time, grid-based models often process images unnecessarily dense introducing large time and memory requirements. Therefore it is still a challenging problem to deploy recent methods in the clinical setting. Evaluating images on only a limited number of locations has the potential to overcome those limitations and may also enable the acquisition of medical images using adaptive sparse sampling, which could substantially reduce scan times and radiation doses. In this work we investigate the problem of semantic edge detection in CT and X-ray images from sparse sampling locations. We propose a deep learning architecture that comprises of two parts: 1) a lightweight fully convolutional CNN to extract informative sampling points and 2) our novel sparse structured prediction network (SSPNet). The SSPNet processes image patches on a graph generated from the sampled locations and outputs semantic edge activations for each patch which are accumulated in an array via a weighted voting scheme to recover a dense prediction. We conduct several ablation experiments for our network on a dataset consisting of 10 abdominal CT slices from VISCERAL and evaluate its performance against strong baseline UNets on the JSRT database of chest X-rays.

Cite this Paper


BibTeX
@InProceedings{pmlr-v102-hansen19a, title = {Sparse Structured Prediction for Semantic Edge Detection in Medical Images}, author = {Hansen, Lasse and Heinrich, {Mattias P.}}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {250--259}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/hansen19a/hansen19a.pdf}, url = { http://proceedings.mlr.press/v102/hansen19a.html }, abstract = {In medical image analysis most state-of-the-art methods rely on deep neural networks with learned convolutional filters. For pixel-level tasks, e.g. multi-class segmentation, approaches build upon UNet-like encoder-decoder architectures show impressive results. However, at the same time, grid-based models often process images unnecessarily dense introducing large time and memory requirements. Therefore it is still a challenging problem to deploy recent methods in the clinical setting. Evaluating images on only a limited number of locations has the potential to overcome those limitations and may also enable the acquisition of medical images using adaptive sparse sampling, which could substantially reduce scan times and radiation doses. In this work we investigate the problem of semantic edge detection in CT and X-ray images from sparse sampling locations. We propose a deep learning architecture that comprises of two parts: 1) a lightweight fully convolutional CNN to extract informative sampling points and 2) our novel sparse structured prediction network (SSPNet). The SSPNet processes image patches on a graph generated from the sampled locations and outputs semantic edge activations for each patch which are accumulated in an array via a weighted voting scheme to recover a dense prediction. We conduct several ablation experiments for our network on a dataset consisting of 10 abdominal CT slices from VISCERAL and evaluate its performance against strong baseline UNets on the JSRT database of chest X-rays.} }
Endnote
%0 Conference Paper %T Sparse Structured Prediction for Semantic Edge Detection in Medical Images %A Lasse Hansen %A Mattias P. Heinrich %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-hansen19a %I PMLR %P 250--259 %U http://proceedings.mlr.press/v102/hansen19a.html %V 102 %X In medical image analysis most state-of-the-art methods rely on deep neural networks with learned convolutional filters. For pixel-level tasks, e.g. multi-class segmentation, approaches build upon UNet-like encoder-decoder architectures show impressive results. However, at the same time, grid-based models often process images unnecessarily dense introducing large time and memory requirements. Therefore it is still a challenging problem to deploy recent methods in the clinical setting. Evaluating images on only a limited number of locations has the potential to overcome those limitations and may also enable the acquisition of medical images using adaptive sparse sampling, which could substantially reduce scan times and radiation doses. In this work we investigate the problem of semantic edge detection in CT and X-ray images from sparse sampling locations. We propose a deep learning architecture that comprises of two parts: 1) a lightweight fully convolutional CNN to extract informative sampling points and 2) our novel sparse structured prediction network (SSPNet). The SSPNet processes image patches on a graph generated from the sampled locations and outputs semantic edge activations for each patch which are accumulated in an array via a weighted voting scheme to recover a dense prediction. We conduct several ablation experiments for our network on a dataset consisting of 10 abdominal CT slices from VISCERAL and evaluate its performance against strong baseline UNets on the JSRT database of chest X-rays.
APA
Hansen, L. & Heinrich, M.P.. (2019). Sparse Structured Prediction for Semantic Edge Detection in Medical Images. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:250-259 Available from http://proceedings.mlr.press/v102/hansen19a.html .

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