2015 Summer Project Week:BigDataFeatures

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  Projects List

Key Investigators

• Matthew Toews, École de Technologie Supérieure
• William Wells, BWH, Harvard Medical School
• Raul San Jose Estepar, BWH, Harvard Medical School
• Tina Kapur, BWH, Harvard Medical School
• Utsav Pardasani, Robarts (Observing!)
• Salvatore Scaramuzzino (Interested!)
• Andrey Fedorov, BWH

3D SIFT-Rank Visualization, SLC 2015, IPMI 2015

Lung CT Features

Data Reduction for 20000 lung CT volumes

Prostate US Features

Project Description

Objective

• This project will investigate the use of 3D SIFT-RANK image features for organizing and deriving information from 3D medical image volumes.
• Technology: invariant feature extraction, descriptor representation.
• Application domains: registration, segmentation, classification.
• Image domains: lung CT, brain MR, prostate and brain ultrasound.
• Clinical use case scenarios: chronic obstructive pulmonary disease, Alzheimer's disease, cancer.

Approach, Plan

• Discussion and documentation
• Algorithms: fast KNN methods, hashing, robust estimation (RANSAC, Hough transform).
• Mathematical formalisms: probabilistic inference, kernel methods, manifold learning.

Progress

1) Discussed applications of 3D SIFT-Rank

• Analyzing COPD - lung CT - Raul San Jose Estepar
• Identifying similar cases - MR/CT - Tobias Penzkofer
• Infant brain analysis - MR - Steve Pieper
• Prostate segmentation - MR/US - Andrey Federov
• Galaxy classification - Astronomical Radio Data Cube (lambda=21cm) - Davide Punzo

2) Coding for kernel regression framework (C++)

References

[1] SIFT View, NAMIC 2015 SLC Project Week
[2] "A Feature-based Approach to Big Data Analysis of Medical Images", M. Toews, C. Wachinger, R. S. J. Estepar, W.M. Wells III. Information Processing in Medical Imaging (IPMI), 2015.
[3] "Keypoint Transfer Segmentation", C. Wachinger, M. Toews, G. Langs, W.M. Wells IIIi, P. Golland. Information Processing in Medical Imaging (IPMI), 2015.