Circle Representation for Medical Instance Object Segmentation

• URL: http://arxiv.org/abs/2403.11507v1
• Date: Mon, 18 Mar 2024 06:25:41 GMT
• Title: Circle Representation for Medical Instance Object Segmentation
• Authors: Juming Xiong, Ethan H. Nguyen, Yilin Liu, Ruining Deng, Regina N Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, Haichun Yang, Agnes B. Fogo, Yuankai Huo,
• Abstract summary: We introduce CircleSnake, a simple end-to-end segmentation approach that utilizes circle contour deformation for segmenting ball-shaped medical objects at the instance level. In practical applications, such as the detection of glomeruli, nuclei, and eosinophils in pathological images, CircleSnake has demonstrated superior performance and greater rotation invariance.
• Score: 6.4832235108711345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e.g., cells, glomeruli, and nuclei). Given its outstanding effectiveness in instance detection, it is compelling to consider the application of circle representation for segmenting instance medical objects. In this study, we introduce CircleSnake, a simple end-to-end segmentation approach that utilizes circle contour deformation for segmenting ball-shaped medical objects at the instance level. The innovation of CircleSnake lies in these three areas: (1) It substitutes the complex bounding box-to-octagon contour transformation with a more consistent and rotation-invariant bounding circle-to-circle contour adaptation. This adaptation specifically targets ball-shaped medical objects. (2) The circle representation employed in CircleSnake significantly reduces the degrees of freedom to two, compared to eight in the octagon representation. This reduction enhances both the robustness of the segmentation performance and the rotational consistency of the method. (3) CircleSnake is the first end-to-end deep instance segmentation pipeline to incorporate circle representation, encompassing consistent circle detection, circle contour proposal, and circular convolution in a unified framework. This integration is achieved through the novel application of circular graph convolution within the context of circle detection and instance segmentation. In practical applications, such as the detection of glomeruli, nuclei, and eosinophils in pathological images, CircleSnake has demonstrated superior performance and greater rotation invariance when compared to benchmarks. The code has been made publicly available: https://github.com/hrlblab/CircleSnake.

Related papers

• Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results [9.540862304334969]
  We propose Weighted Circle Fusion (WCF), a simple approach for merging predictions from various circle detection models. Our method undergoes thorough evaluation on a proprietary dataset for glomerular detection in object detection within whole slide imaging (WSI) The findings reveal a performance gain of 5 %, respectively, compared to existing ensemble methods.
  arXiv  Detail & Related papers  (2024-06-27T21:34:51Z)
• AdaContour: Adaptive Contour Descriptor with Hierarchical Representation [52.381359663689004]
  Existing angle-based contour descriptors suffer from lossy representation for non-star shapes. AdaCon is able to represent shapes more accurately robustly than other descriptors.
  arXiv  Detail & Related papers  (2024-04-12T07:30:24Z)
• CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention [13.947162082687417]
  We present CircleFormer, a Transformer-based circular medical object detection with dynamic anchor circles. We evaluate our method in circular nuclei detection and segmentation on the public MoNuSeg dataset.
  arXiv  Detail & Related papers  (2023-08-30T17:01:01Z)
• CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame [60.24797081117877]
  We propose the CRIN, namely Centrifugal Rotation-Invariant Network. CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations. A continuous distribution for 3D rotations based on points is introduced.
  arXiv  Detail & Related papers  (2023-03-06T13:14:10Z)
• Recurrent Generic Contour-based Instance Segmentation with Progressive Learning [111.31166268300817]
  We propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance.
  arXiv  Detail & Related papers  (2023-01-21T05:34:29Z)
• CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection [62.41288479917261]
  We propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans look at low resolution and occluded objects. Experiments on two pedestrian detection datasets, Caltech and CityPersons, show that CircleNet improves the performance of occluded and low-resolution pedestrians with significant margins.
  arXiv  Detail & Related papers  (2022-12-12T04:10:24Z)
• Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections [58.720142291102135]
  We propose a new iterative method which we term Iterative Next Boundary Detection (INBD) It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order.
  arXiv  Detail & Related papers  (2022-12-06T14:49:41Z)
• CircleSnake: Instance Segmentation with Circle Representation [4.009829991224921]
  We propose CircleSnake, a simple end-to-end circle contour deformation-based segmentation method for ball-shaped medical objects. Compared to the prevalent DeepSnake method, our contribution is three-fold. The proposed CircleSnake method is the first end-to-end circle representation deep segmentation pipeline method.
  arXiv  Detail & Related papers  (2022-11-02T16:34:20Z)
• Circle Representation for Medical Object Detection [5.359910146589289]
  Box representation is efficacious but not necessarily optimized for biomedical objects. We propose a simple circle representation for medical object detection and introduce CircleNet. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance.
  arXiv  Detail & Related papers  (2021-10-22T23:16:42Z)
• CircleNet: Anchor-free Detection with Circle Representation [10.081591705078678]
  CircleNet is a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus. We evaluate CircleNet in the context of detection of glomerulus.
  arXiv  Detail & Related papers  (2020-06-03T18:31:51Z)
• A Rotation-Invariant Framework for Deep Point Cloud Analysis [132.91915346157018]
  We introduce a new low-level purely rotation-invariant representation to replace common 3D Cartesian coordinates as the network inputs. Also, we present a network architecture to embed these representations into features, encoding local relations between points and their neighbors, and the global shape structure. We evaluate our method on multiple point cloud analysis tasks, including shape classification, part segmentation, and shape retrieval.
  arXiv  Detail & Related papers  (2020-03-16T14:04:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.