Department of Medical Physics

The Harini Veeraraghavan Lab

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Research

Harini Veeraraghavan
Harini Veeraraghavan, PhD
Associate Member

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.

View Lab Overview

Research Projects

Our AI models identify features that best signal contrast between tumor and background for highly accurate tumor segmentation.
Our AI models can track voxel-level changes in tumors and organs to provide explainable biomarkers of treatment outcomes.
Our AI image foundation models segment organs from brain to pelvis and are capable of segmenting tumors from multiple imaging modalities.

Publications Highlights

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/

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People

Harini Veeraraghavan

Harini Veeraraghavan, PhD

Associate Member

  • 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.

Members

Sulov Chalise
Min Seo Chloe Choi
Sudarshan Madhavan
Aneesh Rangnekar
Josiah Simeth
Wei Zhao
Lab Alumni
Duc Fehr

Goodyear Inc.

Jue Jiang

Machine Learning Scientist

Jue Jiang

Machine Learning Scientist, MSK

Peter Klages

Medical Physicist, Princess Margaret Cancer Center

Florent Tixier

Nationwide Children’s Hospital

Hyemin Um

Case Western Reserve University, pursuing PhD

Lab Affiliations

Achievements

  • 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.

Open Positions

To learn more about available postdoctoral opportunities, please visit our Career Center

To learn more about compensation and benefits for postdoctoral researchers at MSK, please visit Resources for Postdocs

Career Opportunities

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.

Apply now

Disclosures

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.

MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.

Harini Veeraraghavan discloses the following relationships and financial interests:

No disclosures meeting criteria for time period


The information published here is for a specific annual disclosure period. There may be differences between information on this and other public sites as a result of different reporting periods and/or the various ways relationships and financial interests are categorized by organizations that publish such data.

This page and data include information for a specific MSK annual disclosure period (January 1, 2022 through disclosure submission in spring 2023). This data reflects interests that may or may not still exist. This data is updated annually.

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