11 papers · 1 filter
A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
Tanjin Taher Toma, Tejas Sudharshan Mathai, Bikash Santra +11
Accurate segmentation of pheochromocytoma (PCC) in abdominal CT scans is essential for tumor burden estimation, prognosis, and treatment planning. It may also help infer genetic cl…
Classification of Multi-Parametric Body MRI Series Using Deep Learning
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm +2
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrec…
Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee +3
Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier. M…
Longitudinal Assessment of Lung Lesion Burden in CT
Tejas Sudharshan Mathai, Benjamin Hou, Ronald M. Summers
In the U.S., lung cancer is the second major cause of death. Early detection of suspicious lung nodules is crucial for patient treatment planning, management, and improving outcome…
Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
Anisa V. Prasad, Tejas Sudharshan Mathai, Pritam Mukherjee +2
An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior research on pancreas segmentation…
Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
Peter D. Erickson, Tejas Sudharshan Mathai, Ronald M. Summers
Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been devel…