Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
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.
Multi-Scale Feature Fusion with Image-Driven Spatial Integration for Left Atrium Segmentation from Cardiac MRI Images
Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced magnetic resonance imaging plays a vital role in visualizing diseased atrial structures, enabling the diagnosis and management of cardiovascular diseases. It is particularly essential for planning treatment with ablation therapy, a key intervention for atrial fibrillation (AF). However, manual segmentation is time-intensive and prone to inter-observer variability, underscoring the need for automated solutions. Class-agnostic foundation models like DINOv2 have demonstrated remarkable feature extraction capabilities in vision tasks. However, their lack of domain specificity and task-specific adaptation can reduce spatial resolution during feature extraction, impacting the capture of fine anatomical detail in medical imaging. To address this limitation, we propose a segmentation framework that integrates DINOv2 as an encoder with a UNet-style decoder, incorporating multi-scale feature fusion and input image integration to enhance segmentation accuracy. The learnable weighting mechanism dynamically prioritizes hierarchical features from different encoder blocks of the foundation model, optimizing feature selection for task relevance. Additionally, the input image is reintroduced during the decoding stage to preserve high-resolution spatial details, addressing limitations of downsampling in the encoder. We validate our approach on the LAScarQS 2022 dataset and demonstrate improved performance with a 92.3% Dice and 84.1% IoU score for giant architecture compared to the nnUNet baseline model. These findings emphasize the efficacy of our approach in advancing the field of automated left atrium segmentation from cardiac MRI.
Exploring the performance and robustness of Vision foundation models
Project description coming soon.
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: 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>talks
Talk 1 on Relevant Topic in Your Field
Conference Proceeding talk 3 on Relevant Topic in Your Field
teaching
Teaching Assistant
Teaching Assistant