Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Abstract:Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complexmorphological manifestations of the structures, all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Manual segmentation relying of heuristics and experience therefore remains the preferred approach. Nonetheless, this process is tedious, with 100s of structures present inside a single cell. Thus, generating analytic across large population of cells or performing advanced artificial intelligence (AI) tasks such as tracking is greatly limited. We bring modeling and deep learning to a nexus for solving this GT-hard problem, improving' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["artificial intelligence","cardiomyoblasts","deep learning","fluorescence microscopy","live-cell","mitochondria","segmentation"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Abstract:Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complexmorphological manifestations of the structures, all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Manual segmentation relying of heuristics and experience therefore remains the preferred approach. Nonetheless, this process is tedious, with 100s of structures present inside a single cell. Thus, generating analytic across large population of cells or performing advanced artificial intelligence (AI) tasks such as tracking is greatly limited. We bring modeling and deep learning to a nexus for solving this GT-hard problem, improving' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Abstract:Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complexmorphological manifestations of the structures, all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Manual segmentation relying of heuristics and experience therefore remains the preferred approach. Nonetheless, this process is tedious, with 100s of structures present inside a single cell. Thus, generating analytic across large population of cells or performing advanced artificial intelligence (AI) tasks such as tracking is greatly limited. We bring modeling and deep learning to a nexus for solving this GT-hard problem, improving
artificial intelligence; cardiomyoblasts; deep learning; fluorescence microscopy; live-cell; mitochondria; segmentation