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Small Animal Quantitative Imaging

Key Investigators

Project Description

For this project, we aim to bring small animal MR datasets in DICOM format and repeat the process developed for the QIICR program to segment a lesion (a Neuroendocrine Tumor in this case), convert the segmentation to a DICOM segmentation using the DCMQI slicer extension, and finally measure the segmentation using the Quantitative Reporting module. Our aim is to develop a set of repeatable analysis steps we can put into place to analyze additional datasets in our lab.

Objective

  1. Develop a set of processing steps for lesion analysis that are repeatable for other small animal datasets. Access if clinical tools from the QIICR program will apply to small animal MR datasets as well.

Approach and Plan

  1. Test DCMQI tools on a Small Animal MR dataset. Access if small animal DICOM headers are similar enough to clinical scanners.
  2. Use Slicer segmentation tools to generate a label map of the lesion.
  3. Create a DICOM segmentation object form the segmented label using DCMQI
  4. Investigate DeepInfer models for automatic segmentation of later lesions

Progress and Next Steps

Illustrations

Neuroendocrine Liver lesion as DICOM segmentation object

Background and References