Stanford Simbios group

• 
• 
• 

Aim

To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

Process Flowchart

Progress

Atlas Generation from Input MR Images

Model Generation from Input MR Images

Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format). This models can be prepared using the hypermesh software. The models contain vertices of triangles representing femur, tibia and patella regions.

Pre-Segmented Femur/Patella/Tibia Model

Create a filled label map using PolyDataToFilledLabelMap module in slicer

The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.

• 
• 
• 

EM Segmentation based on the atlas

The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.

EM Segmented Output

Register Images

Register Images Module in Slicer

We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We found that Pipelined Bspline Registration would be the best of all registration methods for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used Pipelined BSpline Image registration method for registration. Also we gave one point as landmark point.

• 
• 
• 

Multi Image Registration

We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

• Slice 1:
• 
• Slice 2:
• 
• Mean Slice of above two :
• 

• Slice 1:
• 
• Slice 2:
• 
• Mean Slice of above two :
• 

Datasets

Below are the datasets that we used in our experiments. All the images are in standard dicom format.

Dataset for Patient 58

File:58 IM-0009-0001.zip

Dataset for Patient 57

File:57 IM-0009-0001.zip

Dataset for Patient 64

File:64 IM-0009-0001.zip

Dataset for Patient 65

File:65 IM-0009-0001.zip