Department of Medical Physics
The Harini Veeraraghavan Lab
Associate Attending Computer Scientist Harini Veeraraghavan’s lab develops and translates new AI and machine learning tools diagnosing and personalizing cancer treatments through automated segmentation of normal tissues and tumors applied to radiation treatment automation, early predicting treatment response and toxicity prediction, and longitudinal tumor treatment response monitoring from medical images.
Jiang J, Hong J, Tringale K, Reyngold M, Crane C, Tyagi N, Veeraraghavan H, “Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT”, Medical Physics. 2023. https://pubmed.ncbi.nlm.nih.gov/37265185/
Simeth J, Jiang J, Nosov A, Wibmer A, Zelefsky M, Tyagi N, Veeraraghavan H, “Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer”, Medical Physics. 2023. https://pubmed.ncbi.nlm.nih.gov/36856092/
Jiang J, Tyagi N, Tringale K, Crane C, Veeraraghavan H, “Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)”, Medical Image Computing and Computer Assisted Interventions 2022. https://pubmed.ncbi.nlm.nih.gov/36468915/
Jiang J, Elguindi S, Berry SL, Onochie I, Cervino L, Deasy JO, Veeraraghavan H. “Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT”, Medical Physics, 2022. https://pubmed.ncbi.nlm.nih.gov/35598077/ (This model is used for auto segmentation of head and neck organs in the MSK radiotherapy clinic).
Thompson HM, Kim JK, Jimenez-Rodriguez RM, Garcia-Aguilar J, Veeraraghavan H. “Deep-learning based model for identifying tumor in endoscopic images from patients with locally advanced rectal cancer treated with total neoadjuvant chemotherapy”, Diseases of Colon and Rectum, 2022. https://pubmed.ncbi.nlm.nih.gov/35358109/
Harini Veeraraghavan, PhD
- Associate Attending Computer Scientist Harini Veeraraghavan's lab develops and translates new AI and machine learning tools diagnosing and personalizing cancer treatments through automated segmentation of normal tissues and tumors applied to radiation treatment automation, early predicting treatment response and toxicity prediction, and longitudinal tumor treatment response monitoring from medical images.
- Dr. Veeraraghavan was awarded an NIBIB R01 grant as M-PI (with PI Dr. Neelam Tyagi) for developing AI-based virtual digital twin MRI images for developing and validating longitudinal registration and dose accumulation methods applied to pancreatic cancer patients treated with MRI guided radiation treatments.
- Dr. Veeraraghavan was awarded an NIH R01 grant as contact-PI (with M-PI Dr. Andreas Rimner) for improving safety of lung cancer radiotherapies using AI-based segmentation and tracking of tumor and tissue changes on weekly cone-beam CTs.
- Dr. Veeraraghavan was awarded an Elekta research grant with M-PI Dr. Neelam Tyagi where we are working on developing and evaluating the automated segmentation and tracking of dominant index prostatic lesions from MRI.
- The Veeraraghavan Lab has successfully implemented an AI auto-segmentation solution for radiotherapy planning for head and neck cancer (May 2020) and lung cancer (July 2021), with an MRI-based automated segmentation and registration solution for upper GI organs used for pancreatic cancer treatments under testing prior to release into clinic.
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The Veeraraghavan Lab has multiple openings involving developing deep learning methods for longitudinal tumor treatment response monitoring, image registration for dose accumulation, as well as developing machine learning methods with sparse and heteromodal data sets for response prediction.
Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.
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Harini Veeraraghavan discloses the following relationships and financial interests:
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