open source segmentation tools (Oxford Instruments)
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Oxford Instruments
open source segmentation tools
Open Source Segmentation Tools, supplied by Oxford Instruments, used in various techniques. Bioz Stars score: 99/100, based on 44673 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/open+source+segmentation+tools/Imaris/pm41223649-86-11-26
Average 99 stars, based on 44673 article reviews
Open Source Segmentation Tools, supplied by Oxford Instruments, used in various techniques. Bioz Stars score: 99/100, based on 44673 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/open+source+segmentation+tools/Imaris/pm41223649-86-11-26
Average 99 stars, based on 44673 article reviews
open source segmentation tools - by Bioz Stars,
2026-11
99/100 stars
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Related Articles
Software:Article Title: ClinSegAI: A post-processing framework for superior histopathology segmentation accuracy, radiomics feature preservation, and quantitative analysis. Article Snippet: Accurate cell segmentation underpins reliable radiomics and multi-omic analysis in digital pathology, yet foundation-scale models often output masks that require further refinement for clinical use.. This study presents ClinSegAI, a post-processing tool designed to refine cell segmentation outputs from the BiomedParse foundation model to preserve radiomic feature integrity in hematoxylin and eosin (H&E) stained whole slide images.. The proposed pipeline analyzes whole-slide images, applies preprocessing, and uses BiomedParse for initial nucleus segmentation, followed by a refinement algorithm that corrects segmentation boundaries and merges or splits regions as needed to maintain morphological fidelity. Microscopy:Article Title: ClinSegAI: A post-processing framework for superior histopathology segmentation accuracy, radiomics feature preservation, and quantitative analysis. Article Snippet: Accurate cell segmentation underpins reliable radiomics and multi-omic analysis in digital pathology, yet foundation-scale models often output masks that require further refinement for clinical use.. This study presents ClinSegAI, a post-processing tool designed to refine cell segmentation outputs from the BiomedParse foundation model to preserve radiomic feature integrity in hematoxylin and eosin (H&E) stained whole slide images.. The proposed pipeline analyzes whole-slide images, applies preprocessing, and uses BiomedParse for initial nucleus segmentation, followed by a refinement algorithm that corrects segmentation boundaries and merges or splits regions as needed to maintain morphological fidelity. |