CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion

• URL: http://arxiv.org/abs/2403.01513v1
• Date: Sun, 3 Mar 2024 13:36:07 GMT
• Title: CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion
• Authors: Jiao Ding, Jie Chang, Renrui Han, Li Yang
• Abstract summary: CDSE-UNet is a novel UNet-based segmentation model that integrates Canny operator edge detection and a dual-path SENet feature fusion mechanism. We have developed a Multiscale Convolution approach, replacing the standard Convolution in UNet, to adapt to the varied lesion sizes and shapes. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models.
• Score: 10.831487161893305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentation model that integrates Canny operator edge detection and a dual-path SENet feature fusion mechanism. This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the Double SENet Feature Fusion Block, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution approach, replacing the standard Convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models, particularly in segmenting large and small lesion areas, accurately delineating lesion edges, and effectively suppressing noise

Related papers

• COVID-CT-H-UNet: a novel COVID-19 CT segmentation network based on attention mechanism and Bi-category Hybrid loss [7.139873310466422]
  COVID-19 outbreak has emerged as a crucial focus in healthcare research. supplementing RT-PCR with the pathological study of COVID-19 through CT imaging has become imperative. In this paper, we propose COVID-CT-H-UNet, a COVID-19 CT segmentation network to solve these problems.
  arXiv  Detail & Related papers  (2024-03-16T10:25:07Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Polyp Segmentation [52.06525450636897]
  Automatic polyp segmentation plays a crucial role in the early diagnosis and treatment of colorectal cancer. Existing methods rely heavily on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised polyp (DEC-Seg) from colonoscopy images.
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images [23.496487874821756]
  We propose a domain adaptation based self-correction model (DASC-Net) for COVID-19 infection segmentation on CT images. DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine results. Extensive experiments over three publicly available COVID-19 CT datasets demonstrate that DASC-Net consistently outperforms state-of-the-art segmentation, domain shift, and coronavirus infection segmentation methods.
  arXiv  Detail & Related papers  (2021-04-20T00:45:01Z)
• Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT [1.6815638149823744]
  COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world. In this paper, a multi-class COVID-19 CT segmentation is proposed aiming at helping radiologists estimate the extent of effected lung volume.
  arXiv  Detail & Related papers  (2021-03-09T16:48:15Z)
• Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks [0.8224695424591678]
  This work proposes a two-stage deep Convolutional Neural Networks (CNNs) based framework for delineation of COVID-19 infected regions in Lung CT images. In the first stage, COVID-19 specific CT image features are enhanced using a two-level discrete wavelet transformation. These enhanced CT images are then classified using the proposed custom-made deep CoV-CTNet. In the second stage, the CT images classified as infectious images are provided to the segmentation models for the identification and analysis of COVID-19 infectious regions.
  arXiv  Detail & Related papers  (2020-09-16T02:28:46Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation [14.807364495808779]
  We propose and evaluate a coarse-to-fine fusion convolutional network (CF2-Net) based on a novel feature integration strategy (forming an 'E'-like type) for BUS image segmentation. The proposed CF2-Net was evaluated on an open dataset by using four-fold cross validation. The results of the experiment demonstrate that the CF2-Net obtains state-of-the-art performance when compared with other deep learning-based methods.
  arXiv  Detail & Related papers  (2020-03-23T09:27:26Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)

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.