Difference between revisions of "2017 Winter Project Week/Needle Segmentation from MRI"
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* We have manually segmented around 1k needles from GYN brachytherapy cases. We want to use this data in a supervised learning approach. | * We have manually segmented around 1k needles from GYN brachytherapy cases. We want to use this data in a supervised learning approach. | ||
| − | * We | + | * We have a pipeline for preprocessing the data (image spacing and normalization) |
| + | * TODO: choose between 2 strategies: | ||
| + | ** Continue with the binary classification approach -> in the region of interest, voxels are classified as positive (needle) or negative (background). Challenge: link those voxels together to form needles / filter out false negatives. Alternative: try to find the tip position and use the previous algorithm to segment the needle | ||
| + | ** Use a semantic segmentation approach (fully convolutional neural networks [J Long 2015], Region-based semantic segmentation with end-to-end training [Caesar 2016]) | ||
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Revision as of 16:31, 31 December 2016
Home < 2017 Winter Project Week < Needle Segmentation from MRI
Key Investigators
- Ziyang Wang, BWH
- Alireza Mehrtash, BWH
- Alireza Ziaei Torbati, BWH
- Guillaume Pernelle, Imperial College London
- Tina Kapur, BWH/HMS
Project Description
This project is a continuation of the project started during the 2016 summer project week ( Needle Segmentation from MRI), NeedleFinder offers tools to segment needles from MRI/CT. It has mostly been tested on MRI from GYN brachytherapy cases. Currently the user must provide the needle tip for the segmentation to start. We aim to detect the tip automatically to make the needle segmentation fully automatic.
Objective
- Automatic detection of needle tips in MRI from GYN brachytherapy cases.
Approach, Plan
- We have manually segmented around 1k needles from GYN brachytherapy cases. We want to use this data in a supervised learning approach.
- We have a pipeline for preprocessing the data (image spacing and normalization)
- TODO: choose between 2 strategies:
- Continue with the binary classification approach -> in the region of interest, voxels are classified as positive (needle) or negative (background). Challenge: link those voxels together to form needles / filter out false negatives. Alternative: try to find the tip position and use the previous algorithm to segment the needle
- Use a semantic segmentation approach (fully convolutional neural networks [J Long 2015], Region-based semantic segmentation with end-to-end training [Caesar 2016])
...
