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Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation.

ABSTRACT: Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network ("DCNet"), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate ground truth data by 77.1% compared to bounding box labeling. Yet centre point annotation still enabled high accuracy when training DCNet on a multi-class algorithm on whole cell features, matching human experts in all 5 object classes in average precision and outperforming humans in consistency. The efficacy and efficiency of the DCNet end-to-end system represents a significant progress toward an open source, fully computationally approach to differential cell count based diagnosis that can be adapted to any pathology need.

SUBMITTER: Lee SMW

PROVIDER: S-EPMC8377024 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation.

Lee Sarada M W SMW Shaw Andrew A Simpson Jodie L JL Uminsky David D Garratt Luke W LW

Scientific reports 20210819 1

Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network ("DCNet"), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell cl ...[more]

PMID: 34413367

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Project description:Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment.On average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance.By using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone.

Dataset Information

Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation.

Publications

Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation.

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