DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Top Cited Papers
Open Access
- 1 June 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 41 (7), 1559-1572
- https://doi.org/10.1109/tpami.2018.2840695
Abstract
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.Keywords
Other Versions
Funding Information
- Wellcome Trust (WT101957)
- Engineering and Physical Sciences Research Council (NS/A000027/1, EP/H046410/1, EP/J020990/1, EP/K005278)
- Wellcome/EPSRC (203145Z/16/Z)
- National Institute for Health Research (NIHR BRC UCLH/UCL)
- Royal Society (RG160569)
This publication has 50 references indexed in Scilit:
- Learning Graphical Model Parameters with Approximate Marginal InferenceIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- The making of fetal surgeryPrenatal Diagnosis, 2010
- Fast High‐Dimensional Filtering Using the Permutohedral LatticeComputer Graphics Forum, 2010
- Interactive segmentation of image volumes with Live SurfaceComputers & Graphics, 2007
- User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliabilityNeuroImage, 2006
- GIST: an interactive, GPU-based level set segmentation tool for 3D medical imagesMedical Image Analysis, 2004
- "GrabCut"ACM Transactions on Graphics, 2004
- An experimental comparison of min-cut/max- flow algorithms for energy minimization in visionIeee Transactions On Pattern Analysis and Machine Intelligence, 2004
- What energy functions can be minimized via graph cuts?Ieee Transactions On Pattern Analysis and Machine Intelligence, 2004
- Snakes, shapes, and gradient vector flowIEEE Transactions on Image Processing, 1998