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Segmentation of Clinically Acquired Medical Images

Primary Investigator/s:
Bennett Landman

Brief Description of Project:

The Human BioMolecular Atlas Program (HuBMAP) provides an opportunity to contextualize findings across cellular to organ systems levels. Constructing an atlas target is the primary endpoint for generalizing anatomical information across scales and populations. An initial target of HuBMAP is the kidney organ and arterial phase contrast-enhanced computed tomography (CT) provides distinctive appearance and anatomical context on the internal substructure of kidney organs such as renal context, medulla, and pelvicalyceal system. With the confounding effects of demographics and morphological characteristics of the kidney across large-scale imaging surveys, substantial variation is demonstrated with the internal substructure morphometry and the intensity contrast due to the variance of imaging protocols. Such variability increases the level of difficulty to localize the anatomical features of the kidney substructure in a well-defined spatial reference for clinical analysis. In order to stabilize the localization of kidney substructures in the context of this variability, we propose a high-resolution CT kidney substructure atlas template. This project will study deep learning preprocessing techniques to extract the volumetric interest of the abdominal regions and further perform a deep supervised registration pipeline to stably adapt the anatomical context of the kidney internal substructure.

Desired Qualifications:
Experience with imaging

Nature of Supervision:
Summer internship (full time)

A Brief Research Plan (period is for 10 weeks):
2 weeks - project overview and plan development
6 weeks - project implementation
1 week - statistical evaluation
1 week - project write-up

Number of Slots: 2

Contact Information
Bennett Landman
Department/s: Electrical & Computer Engineering