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CiiS Lab
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Russell Taylor
127 Hackerman Hall
rht@jhu.edu

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courses:456:2022:projects:456-2022-28:project-28 [2022/05/20 16:50] – [Technical Approach] mliu90courses:456:2022:projects:456-2022-28:project-28 [2022/05/20 16:53] (current) – [Technical Approach] mliu90
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 In this project, the segmentation of MRI volume is firstly realized based on the baseline model --- the nnU-Net. nnU-Net is desired to provide an appropriate segmentation model for the rest of the work in this project. Four networks are designed to develop: 2D U-Net, 3D U-Net, 3D Low-Resolution U-Net and 3D Cascade U-Net. In this project, the segmentation of MRI volume is firstly realized based on the baseline model --- the nnU-Net. nnU-Net is desired to provide an appropriate segmentation model for the rest of the work in this project. Four networks are designed to develop: 2D U-Net, 3D U-Net, 3D Low-Resolution U-Net and 3D Cascade U-Net.
  
-{{ :courses:456:2022:projects:456-2022-28:mri_seg.png?600 |}}+{{ :courses:456:2022:projects:456-2022-28:mri_seg.png?400 |}}
  
-                                       Illustration of MRI segmentation result.+                                   Illustration of MRI segmentation result.
  
 **CT Segmentation** **CT Segmentation**
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 For the CT segmentation, the conventional threshold-based method is shown to work on CT femur segmentation, and will be used as the baseline method for this project. The threshold-based algorithm introduced by Krčah et al.[9] will be used as the baseline model for the project. For the CT segmentation, the conventional threshold-based method is shown to work on CT femur segmentation, and will be used as the baseline method for this project. The threshold-based algorithm introduced by Krčah et al.[9] will be used as the baseline model for the project.
  
 +{{ :courses:456:2022:projects:456-2022-28:ct_seg_result.png?600 |}}
 The improved model for CT segmentation, if necessary, is planned to be another 3D CNN model. The threshold-based segmentation results (with appropriate manual corrections if needed) will then be used as the ground truth annotations for the improved models. Theoretically, the 3D CNN model for CT segmentation can be modified on the basis of the MRI segmentation model. The improved model for CT segmentation, if necessary, is planned to be another 3D CNN model. The threshold-based segmentation results (with appropriate manual corrections if needed) will then be used as the ground truth annotations for the improved models. Theoretically, the 3D CNN model for CT segmentation can be modified on the basis of the MRI segmentation model.
  
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 The semi-automatic (automatic) registration is realized using (1). use ANTs to provide an initial estimation of the transformation between MRI and CT;(2) use MMI-based registration to compute the final transformation. Using the MRI and CT volume segments, the registration algorithm will then compute the transformation between MRI and CT. Finally, the two segmentation models and the registration model will be integrated together to form an end-to-end system and, if possible, to be integrated with 3D Slicer software. The semi-automatic (automatic) registration is realized using (1). use ANTs to provide an initial estimation of the transformation between MRI and CT;(2) use MMI-based registration to compute the final transformation. Using the MRI and CT volume segments, the registration algorithm will then compute the transformation between MRI and CT. Finally, the two segmentation models and the registration model will be integrated together to form an end-to-end system and, if possible, to be integrated with 3D Slicer software.
 +{{ :courses:456:2022:projects:456-2022-28:mri_ct_reg.png?400 |}}
 ======Dependencies====== ======Dependencies======
  
courses/456/2022/projects/456-2022-28/project-28.1653065404.txt.gz · Last modified: by mliu90




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