CircleSnake: Instance Segmentation with Circle Representation

Nguyen, Ethan H.; Yang, Haichun; Asad, Zuhayr; Deng, Ruining; Fogo, Agnes B.; Huo, Yuankai

doi:10.1007/978-3-031-21014-3_31

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…This modification enhances the model's capability to effectively solve object detection tasks with improved accuracy and precision. 24 In this paper, we extend the CircleSnake from single-label instance segmentation to multi-label instance segmentation.…”

Section: Multi-label Circlesnakementioning

confidence: 99%

“…[19][20][21] All aforementioned instance detection and segmentation methods utilized box representations, which could potentially result in less optimal performance compared to the recently introduced circle representation. [22][23][24] In this paper, we present an advanced iteration of the CircleSnake model, elevating its functionality from single-label to multi-label instance segmentation. Our approach is tailored for the precise instance object segmentation of biomarkers in EoE and comprehensive analysis.…”

Section: Introductionmentioning

confidence: 99%

“…It builds upon the CircleSnake model's foundation as a straightforward end-to-end circle contour deformation-based segmentation method for ball-shaped medical objects, seamlessly integrating deep learning tools with medical image analysis. 24 We demonstrate the effectiveness of the multi-label CircleSnake model in accurately identifying and quantifying the presence of Eos in entire esophageal biopsy Whole-Slide Images (WSIs). Compared to the traditional train-from-scratch Mask R-CNN model and the prevalent DeepSnake model counterpart, CircleSnake achieves superior average precision scores.…”

Section: Introductionmentioning

confidence: 99%

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Eosinophils instance object segmentation on whole slide imaging using multi-label circle representation

Liu,

Deng,

Xiong

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

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Eosinophilic esophagitis (EoE) is a chronic and relapsing disease characterized by esophageal inflammation. Symptoms of EoE include difficulty swallowing, food impaction, and chest pain which significantly impact the quality of life, resulting in nutritional impairments, social limitations, and psychological distress. The diagnosis of EoE is typically performed with a threshold (15 to 20) of eosinophils (Eos) per high-power field (HPF). Since the current counting process of Eos is a resource-intensive process for human pathologists, automatic methods are desired. Circle representation has been shown as a more precise, yet less complicated, representation for automatic instance cell segmentation such as CircleSnake approach. However, the CircleSnake was designed as a single-label model, which is not able to deal with multi-label scenarios. In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos. It extends the original CircleSnake model from a single-label design to a multi-label model, allowing segmentation of multiple object types. Experimental results illustrate the CircleSnake model's superiority over the traditional Mask R-CNN model and DeepSnake model in terms of average precision (AP) in identifying and segmenting eosinophils, thereby enabling enhanced characterization of EoE. This automated approach holds promise for streamlining the assessment process and improving diagnostic accuracy in EoE analysis. The source code has been made publicly available at https://github.com/yilinliu610730/ EoE.

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Section: Multi-label Circlesnakementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Eosinophils instance object segmentation on whole slide imaging using multi-label circle representation

Liu,

Deng,

Xiong

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

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“…[15][16][17] Furthermore, deep learning is increasingly playing on pathological advanced tasks, including gene prediction, 18,19 survival analysis, 20,21 virtual staining, 22 etc. At present, most studies of deep learning in renal pathology focus on glomeruli, which are limited to isolated analysis of single level or staining, including segmentation and detection [23][24][25] and classification. 26,27 This does not model the diagnostic process of a nephrologist.…”

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

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The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.

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CircleSnake: Instance Segmentation with Circle Representation

Cited by 2 publications

References 17 publications

Eosinophils instance object segmentation on whole slide imaging using multi-label circle representation

Eosinophils instance object segmentation on whole slide imaging using multi-label circle representation

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

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