Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Ultrastructural study on human placentae from women subjected to assisted reproductive technology treatments.

Biology of reproduction·2011

Same author

Intravenous busulfan-cyclophosphamide as a preparative regimen before allogeneic hematopoietic stem cell transplantation for adult patients with acute lymphoblastic leukemia.

Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation·2011

Same author

Lathyrane diterpenoids from the roots of Euphorbia micractina and their biological activities.

Journal of natural products·2011

Same author

Quantification of sofalcone in human plasma and urine by high performance liquid chromatography-mass spectrometry.

Journal of pharmaceutical and biomedical analysis·2011

Same author

A densely overlapping gene fragmentation approach improves yeast two-hybrid screens for Plasmodium falciparum proteins.

Molecular and biochemical parasitology·2011

Same author

Seedling growth and metal accumulation of selected woody species in copper and lead/zinc mine tailings.

Journal of environmental sciences (China)·2011

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

ClinSegAI: A post-processing framework for superior histopathology segmentation accuracy, radiomics feature

Prem Bhajaj¹, Saiprakash Nalubolu¹, Bhargavram Gurram¹

¹School of Computing, Watson College of Engineering & Applied Science, Binghamton University, Binghamton, NY, 13902, USA.

Computers in Biology and Medicine

|November 12, 2025

Summary

ClinSegAI refines cell segmentation in digital pathology, improving radiomics analysis. This tool enhances accuracy for better integration of histopathology with other data, aiding prognostic models and treatment prediction.

Keywords:

Deep-learning Histopathology Lung cancer Postprocessing Radiomics Segmentation Spatial-transcriptomics Visual transformer

More Related Videos

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Related Experiment Videos

Last Updated: Jan 11, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Area of Science:

Computational pathology
Digital pathology
Medical image analysis

Background:

Accurate cell segmentation is crucial for radiomics and multi-omic analysis in digital pathology.
Foundation models for segmentation often require refinement for clinical applications.

Purpose of the Study:

To introduce ClinSegAI, a post-processing tool to refine cell segmentation outputs from the BiomedParse foundation model.
To preserve radiomic feature integrity in H&E stained whole slide images.

Main Methods:

ClinSegAI analyzes whole-slide images, preprocesses them, and uses BiomedParse for initial nucleus segmentation.
A refinement algorithm corrects segmentation boundaries, merging or splitting regions to maintain morphological fidelity.
Evaluation involved lung and other cancer pathology slides, comparing ClinSegAI against six alternative segmentation approaches.

Main Results:

ClinSegAI achieved the highest average Dice Similarity Coefficient (DSC) near 0.80.
It significantly reduced segmentation errors, demonstrating the lowest 95th-percentile Hausdorff distance (HD95) and average symmetric surface distance (ASSD).
Refined segmentations better preserved radiomic feature distributions (shape, intensity, texture) compared to ground truth.

Conclusions:

ClinSegAI enhances segmentation accuracy and radiomics reliability in computational pathology.
Improved segmentation enables more reliable integration of histopathology with other modalities for enhanced prognostic models and treatment prediction.
The tool demonstrates the value of targeted post-processing for foundational visual transformer models.