| Chapter title |
Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.
|
|---|---|
| Chapter number | 31 |
| Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
|
| Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, March 2014
|
| DOI | 10.1007/978-3-642-40763-5_31 |
| Pubmed ID | |
| Book ISBNs |
978-3-64-240762-8, 978-3-64-240763-5
|
| Authors |
Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M, Adhish Prasoon, Kersten Petersen, Christian Igel, François Lauze, Erik Dam, Mads Nielsen, Prasoon, Adhish, Petersen, Kersten, Igel, Christian, Lauze, François, Dam, Erik, Nielsen, Mads |
| Abstract |
Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 2 | <1% |
| United States | 2 | <1% |
| Malaysia | 1 | <1% |
| Canada | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Malta | 1 | <1% |
| Taiwan | 1 | <1% |
| Unknown | 495 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 129 | 26% |
| Student > Master | 98 | 19% |
| Researcher | 49 | 10% |
| Student > Bachelor | 32 | 6% |
| Student > Doctoral Student | 22 | 4% |
| Other | 65 | 13% |
| Unknown | 109 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 151 | 30% |
| Engineering | 105 | 21% |
| Medicine and Dentistry | 37 | 7% |
| Agricultural and Biological Sciences | 16 | 3% |
| Biochemistry, Genetics and Molecular Biology | 7 | 1% |
| Other | 36 | 7% |
| Unknown | 152 | 30% |