Nuclei R-CNN: Improve Mask R-CNN for Nuclei Segmentation

Lv, Guofeng; Wen, Ke; Wu, Zheng; Xu, Jin; An, Hong; He, Ji

doi:10.1109/icicsp48821.2019.8958541

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…Another method, to achieve instance objectlevel segmentation, has integrated fully convolutional networks with detection methods on WSIs (e.g., Mask-region-based convolutional neural network 66 ), but there is still room to improve. 67,68 Recently, Jha et al 69 shows that the "detect-then-segment" two-stage segmentation approach yields more accurate results than the conventional single-stage instance segmentation tactics. Following this study, we propose a detect-classify-segment pipeline to achieve even more accurate glomerular segmentation results.…”

Section: Glomerular Segmentationmentioning

confidence: 99%

Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining

Yao

Long

et al. 2022

J. Med. Imag.

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Purpose: The quantitative detection, segmentation, and characterization of glomeruli from highresolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning.Approach: The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSIlevel multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified-GSS (associated with hypertension-related injury), disappearing-GSS (a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent-GSS (nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. Results:The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice dimilarity coefficient. Conclusion:We develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is user-friendly to non-technical users via a single line of command. The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/Glo-In-One.

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Section: Glomerular Segmentationmentioning

confidence: 99%

Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining

Yao

Long

et al. 2022

J. Med. Imag.

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“…Bouteldja et al investigated the concept of active learning for accurate segmentation accuracy [65] by performing a large number of 72,722 expert-based annotations, while Gadermayr et al has proposed a weakly supervised pipeline for segmenting renal glomeruli [66]. Other method, to achieve instance object level segmentation, has integrated fully convolutional networks with detection methods on WSIs(e.g., Mask-RCNN [67]), but there is still room to improve [68], [69].Recently, Aadarsh et al [70] shows that the "detect-then-segment" two-stage segmentation approach yields more accurate results than the conventional single-stage instance segmentation tactics. Following this study, we propose a detect-classify-segment pipeline to achieve even more accurate glomerular segmentation results.…”

Section: Glomerular Segmentationmentioning

confidence: 99%

Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

Yao¹,

Lu²,

Long³

et al. 2022

Preprint

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The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSIlevel multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessedsolidified (S-GGS, associated with hypertension-related injury), disappearing (D-GGS, a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent (O-GGS, nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce selfsupervised deep learning to glomerular quantification via largescale web image mining. The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice Similarity Coefficient (DSC). In this paper, we develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is userfriendly to non-technical users via a single line of command.The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/ Glo-In-One.

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“…In Ref. [20], Unet model is enhanced to light weight model with modified enhanced branch so that it would potentially be able to work with low-resources computing. This model is then applied to Data science bowl 2018 dataset and the limitation is that masks needed to be constructed for removal of isolated objects and small holes within the image.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 3 is describing the UNet architecture. For our paper, we began by creating a UNet model that is based on an earlier architecture known as the FCN, is a CNN that substitutes fully connected layers with an inverted convolutional layer that upsamples the feature map according to the dimensions of the initial input image while keeping the location data [20,21]. Unfortunately, due to excessive downsampling, the FCN's final layer suffers from information loss.…”

Section: Model Architecturementioning

confidence: 99%

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

Sawhney¹,

Sharma²,

Vashishtha³

et al. 2022

RIA

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With the advancement of medicine, there is a terrific requirement to process an increasing number of medical imaging where image segmentation comes into play. Single cell division is generally among the first as well as most essential tasks in image-based cell analysis. The identification of the nuclei allows pathologists to determine each cell in the sample, and by measuring how cells respond to various treatments, they can comprehend the fundamental biological processes in work. In this paper we gathered a deep learning network which detects and parts the cell microscopy image. Highly advanced performance is achieved in image segmentation tasks through deep learning-based techniques. These procedures are complex and need the support of compelling computational resources. This paper emphasizes the basic principles of the methods used to segment an image. We have implemented U-Net for the semantic segmentation of nucleus. A successful implementation will aid researchers immensely in their fight to find pharmaceutical solutions to medical crises while saving both valuable research time and funding.

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Nuclei R-CNN: Improve Mask R-CNN for Nuclei Segmentation

Cited by 15 publications

References 16 publications

Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining

Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining

Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

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