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portfolio
Exploring the performance and robustness of Natural Domain Vision Language models for Segmenting Medical Image
Project description coming soon.
Exploring the performance and robustness of Vision Language models for Image Quality Assessment
Developed a two-stage VLM-LLM framework for automated, clinically grounded image quality assessment of left atrial LGE-MRI scans to support ablation planning in atrial fibrillation patients. A fine-tuned VLM generates structured radiology-style quality reports across four expert-defined criteria, which are then mapped by a GPT-based reasoning module to a binary clinical usability decision. Benchmarked four state-of-the-art VLM architectures on a curated and expert-annotated MRI data.
Exploring the robustness of Vision Foundation Models for Left Atrium Segmentation from Cardiac MR Images
This study explores the robustness of vision foundation models for automated left atrium segmentation from cardiac MR images, addressing the critical challenge of limited annotated medical data in clinical settings. Natural domain foundation models were implemented with different decoder architectures and adaptation techniques to evaluate their segmentation performance on LGE-MRI data. As the lead researcher, I fine-tuned and benchmarked multiple vision transformer-based models.
Evaluating DINOv2 for Left Atrium Segmentation from MRI Images
Accurate left atrium segmentation is crucial for diagnosing and planning treatments for atrial fibrillation. We evaluated the out-of-the-box performance of DINOv2, a self-supervised vision transformer, for segmenting the left atrium from MRI images. With a mean Dice score of 87.1%, DINOv2 outperformed baseline models, demonstrating its robustness even with limited data and minimal fine-tuning. This highlights DINOv2’s potential for broader applications in medical imaging.
Analysis of Non-rigid Registration Techniques
Non-rigid surface-based soft tissue registration is vital for surgical navigation, enabling the integration of pre-operative and intra-operative images for real-time visualization in a common coordinate system. Despite its potential, challenges like complex surface structures and degrees of freedom hinder widespread adoption. This study compares several open-source liver registration algorithms, highlighting the Gaussian Mixture Model-Finite Element Model (GMM-FEM) as the most robust, with consistently lower post-registration errors under reduced visibility and increased surface deformation. This method offers a promising solution for improving surgical navigation accuracy.
publications
Classification and Feature Extraction of Different Hand Movements from EMG Signal using Machine Leaning based Algorithms
Published in International Conference on Electrical, Communication and Computer Engineering (ICECCE) , 2021
Recommended citation: Bipasha Kundu, Dr. Desineni Subarram Naidu. "Classification and Feature Extraction of Different Hand Movements from EMG Signal using Machine Leaning based Algorithms; pp. 1-5. IEEE, 2021. Published in arXiv preprint available, 2024 Recommended citation: Kamrul H Foysal, Bipasha Kundu, Jo Woon Chong. "Temperature Detection from Images Using Smartphones." Published in SPIE MEdical Imaging, 2024 Recommended citation: Bipasha Kundu, Zixin Yang, Richard Simon, and Cristian A Linte. "Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration." SPIE Medical Imaging. Published in SPIE Medical Imaging, 2025 Recommended citation: Bipasha Kundu, Bidur Khanal, Richard Simon, & Cristian A. Linte. "Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images." SPIE Medical Imaging Published in Functional Imaging and Modeling of the Heart (FIMH), 2025 Recommended citation: Bipasha Kundu, Bidur Khanal, Richard Simon, & Cristian A. Linte. "Investigating the Domain Adaptability of General-Purpose Foundation Models for Left Atrium Segmentation from MR Images; FIMH. Published in Engineering in Medicine and Biology Society, 2025 Recommended citation: Bipasha Kundu, Zixin Yang, Richard Simon, and Cristian A Linte. "Multi-Scale Feature Fusion with Image-Driven Spatial Integration for Left Atrium Segmentation from Cardiac MR Image." EMBC. Published in SPIE MEdical Imaging, 2026 Recommended citation: Bipasha Kundu, Richard Simon, and Cristian A Linte. "Motion-Guided Scar Detection from Static Left Atrial MRI via Deformable Registration to a Healthy Atlas" ." SPIE Medical Imaging. Published in Engineering in Medicine and Biology Society (EMBC), 2026 Recommended citation: Bipasha Kundu, & Cristian A. Linte. "A Two Stage Pipeline for Left Atrial Wall Constrained Scar Segmentation and Localization from LGE-MR Images"; EMBC.</p> </article> </div> Published: This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown! Published: This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. Undergraduate Courses (Digital Logic System, Electrical Circuit Analysis, Electronics II, Power Electronics), University of Minnesota Duluth, Electrical Engineering, 2018 Undergraduate and Graduate Level Courses (Radiometry, Interactions between Light and Matter, Human Visual Systems), Rochester Institute of Technology, Imaging Science, 2022
Download Paper</p> </article> </div> Temperature Detection from Images Using Smartphones
Download Paper Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration
Download Paper</p> </article> </div> Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images
Download Paper</p> </article> </div> Investigating the Domain Adaptability of General-Purpose Foundation Models for Left Atrium Segmentation from MR Images
Download Paper</p> </article> </div> Multi-Scale Feature Fusion with Image-Driven Spatial Integration for Left Atrium Segmentation from Cardiac MR Images
Download Paper</p> </article> </div> Motion-Guided Scar Detection from Static Left Atrial MRI via Deformable Registration to a Healthy Atlas
Download Paper</p> </article> </div> A Two Stage Pipeline for Left Atrial Wall Constrained Scar Segmentation and Localization from LGE-MR Images
talks
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