Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
| Autor: | Verena E. Rozanski, Birgit Ertl-Wagner, Fausto Milletari, Kai Bötzel, Johannes Levin, Annika Plate, Juliana Maiostre, Seyed-Ahmad Ahmadi, Olaf Dietrich, Christine Kroll, Nassir Navab |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Network architecture Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning media_common.quotation_subject Computer Science - Computer Vision and Pattern Recognition Scale-space segmentation Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Voting Signal Processing Segmentation Computer vision Computer Vision and Pattern Recognition Artificial intelligence business 030217 neurology & neurosurgery Software media_common Abstraction (linguistics) Curse of dimensionality |
| Zdroj: | Computer Vision and Image Understanding. 164:92-102 |
| ISSN: | 1077-3142 |
| Popis: | In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest. This approach does not only use the CNN classification outcomes, but it also implements voting by exploiting the features produced by the deepest portion of the network. We show that this learning-based segmentation method is robust, multi-region, flexible and can be easily adapted to different modalities. In the attempt to show the capabilities and the behaviour of CNNs when they are applied to medical image analysis, we perform a systematic study of the performances of six different network architectures, conceived according to state-of-the-art criteria, in various situations. We evaluate the impact of both different amount of training data and different data dimensionality (2D, 2.5D and 3D) on the final results. We show results on both MRI and transcranial US volumes depicting respectively 26 regions of the basal ganglia and the midbrain. |
| Databáze: | OpenAIRE |
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