Pelvis Segmentation Using Multi-pass U-Net and Iterative Shape Estimation
| Autor: | Alejandro F. Frangi, Chunliang Wang, Örjan Smedby, Pedro Filipe de Oliveira Lopes, Bryan Connolly |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Deep learning 0206 medical engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Phase (waves) Pattern recognition Computed tomography 02 engineering and technology 020601 biomedical engineering Image (mathematics) 03 medical and health sciences 0302 clinical medicine medicine Segmentation Shape context Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Computational Methods and Clinical Applications in Musculoskeletal Imaging ISBN: 9783030111656 MSKI@MICCAI |
| DOI: | 10.1007/978-3-030-11166-3_5 |
| Popis: | In this report, an automatic method for segmentation of the pelvis in three-dimensional (3D) computed tomography (CT) images is proposed. The method is based on a 3D U-net which has as input the 3D CT image and estimated volumetric shape models of the targeted structures and which returns the probability maps of each structure. During training, the 3D U-net is initially trained using blank shape context inputs to generate the segmentation masks, i.e. relying only on the image channel of the input. The preliminary segmentation results are used to estimate a new shape model, which is then fed to the same network again, with the input images. With the additional shape context information, the U-net is trained again to generate better segmentation results. During the testing phase, the input image is fed through the same 3D U-net multiple times, first with blank shape context channels and then with iteratively re-estimated shape models. Preliminary results show that the proposed multi-pass U-net with iterative shape estimation outperforms both 2D and 3D conventional U-nets without the shape model. |
| Databáze: | OpenAIRE |
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