CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation

• URL: http://arxiv.org/abs/2411.18893v1
• Date: Thu, 28 Nov 2024 03:54:09 GMT
• Title: CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation
• Authors: Huy Trinh, Khang Tran, Nam Nguyen, Tri Cao, Binh Nguyen,
• Abstract summary: We propose using a CovHuSeg algorithm to solve the problem of kidney glomeruli segmentation. This simple post-processing method is specified to adapt to the segmentation of ball-shaped anomalies.
• Score: 3.5751379150024767
• License:
• Abstract: Segmentation has long been essential in computer vision due to its numerous real-world applications. However, most traditional deep learning and machine learning models need help to capture geometric features such as size and convexity of the segmentation targets, resulting in suboptimal outcomes. To resolve this problem, we propose using a CovHuSeg algorithm to solve the problem of kidney glomeruli segmentation. This simple post-processing method is specified to adapt to the segmentation of ball-shaped anomalies, including the glomerulus. Unlike other post-processing methods, the CovHuSeg algorithm assures that the outcome mask does not have holes in it or comes in unusual shapes that are impossible to be the shape of a glomerulus. We illustrate the effectiveness of our method by experimenting with multiple deep-learning models in the context of segmentation on kidney pathology images. The results show that all models have increased accuracy when using the CovHuSeg algorithm.

Related papers

• A Greedy Strategy for Graph Cut [95.2841574410968]
  We propose a greedy strategy to solve the problem of Graph Cut, called GGC. It starts from the state where each data sample is regarded as a cluster and dynamically merges the two clusters. GGC has a nearly linear computational complexity with respect to the number of samples.
  arXiv  Detail & Related papers  (2024-12-28T05:49:42Z)
• Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction [2.264332709661011]
  We propose to replace the soft-skeleton algorithm by a U-Net which computes the vascular skeleton directly from the segmentation. We build upon this network a cascaded U-Net trained with the clDice loss to embed topological constraints during the segmentation.
  arXiv  Detail & Related papers  (2023-07-21T14:12:28Z)
• Boundary-Aware Network for Kidney Parsing [18.75582522299797]
  We propose a boundary-aware network (BA-Net) to segment kidneys on computed tomography angiography (CTA) scans. The model contains a shared encoder, a boundary decoder, and a segmentation decoder. The results demonstrate the effectiveness of the BA-Net.
  arXiv  Detail & Related papers  (2022-08-29T02:19:30Z)
• Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining [5.786726927985774]
  We develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. We release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning.
  arXiv  Detail & Related papers  (2022-05-31T21:22:10Z)
• PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images [83.26057031236965]
  We propose a pixel-wise sparse graph reasoning (PSGR) module to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. The PSGR module avoids imprecise pixel-to-node projections and preserves the inherent information of each pixel for global reasoning. The solution has been evaluated against four widely-used segmentation models on three public datasets.
  arXiv  Detail & Related papers  (2021-08-09T04:58:23Z)
• A Novel Falling-Ball Algorithm for Image Segmentation [0.14337588659482517]
  Region-based Falling-Ball algorithm is presented, which is a region-based segmentation algorithm. The proposed algorithm detects the catchment basins by assuming that a ball falling from hilly terrains will stop in a catchment basin.
  arXiv  Detail & Related papers  (2021-05-06T12:41:10Z)
• Deep Gaussian Processes for Few-Shot Segmentation [66.08463078545306]
  Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples. We propose a few-shot learner formulation based on Gaussian process (GP) regression. Our approach sets a new state-of-the-art for 5-shot segmentation, with mIoU scores of 68.1 and 49.8 on PASCAL-5i and COCO-20i, respectively.
  arXiv  Detail & Related papers  (2021-03-30T17:56:32Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology [1.6506888719932784]
  We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship. We demonstrate the application of our method in medical microscopy image segmentation; particularly, in segmenting renal glomerular micro-environment in renal pathology.
  arXiv  Detail & Related papers  (2020-02-05T01:20:28Z)
• Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images [69.74667930907314]
  We propose a bending loss regularized network for nuclei segmentation. The proposed bending loss defines high penalties to contour points with large curvatures, and applies small penalties to contour points with small curvature. Minimizing the bending loss can avoid generating contours that encompass multiple nuclei.
  arXiv  Detail & Related papers  (2020-02-03T21:20:50Z)

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.