DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation

• URL: http://arxiv.org/abs/2210.13595v1
• Date: Mon, 24 Oct 2022 20:36:30 GMT
• Title: DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation
• Authors: Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci
• Abstract summary: Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time speed are the essential obstacles to be overcome for successful clinical integration of such systems.
• Score: 2.6179759969345002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time speed are the essential obstacles to be overcome for successful clinical integration of such systems. While literature is focused on improving accuracy, the speed parameter is often ignored. Toward this critical need, we intend to develop a novel real-time deep learning-based architecture, DilatedSegNet, to perform polyp segmentation on the fly. DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps. Each of these feature maps is passed through a dilated convolution pooling (DCP) block. The outputs from the DCP blocks are concatenated and passed through a series of four decoder blocks that predicts the segmentation mask. The proposed method achieves a real-time operation speed of 33.68 frames per second with an average dice coefficient of 0.90 and mIoU of 0.83. Additionally, we also provide heatmap along with the qualitative results that shows the explanation for the polyp location, which increases the trustworthiness of the method. The results on the publicly available Kvasir-SEG and BKAI-IGH datasets suggest that DilatedSegNet can give real-time feedback while retaining a high \ac{DSC}, indicating high potential for using such models in real clinical settings in the near future. The GitHub link of the source code can be found here: \url{https://github.com/nikhilroxtomar/DilatedSegNet}.

Related papers

• Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation [3.017482151674131]
  We introduce textittextbfacFANetv2, an advanced encoder-decoder network designed to accurately segment polyps from colonoscopy images. FANetv2 iteratively refines its binary segmentation masks through a novel feedback attention mechanism. It integrates essential information about the number (one or many) and size (small, medium, large) of polyps to further enhance its feature representation capabilities.
  arXiv  Detail & Related papers  (2024-08-25T02:39:45Z)
• BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp Segmentation [0.6062751776009752]
  This research presents BetterNet, a convolutional neural network architecture that combines residual learning and attention methods to enhance the accuracy of polyp segmentation. BetterNet shows promise in integrating computer-assisted diagnosis techniques to enhance the detection of polyps and the early recognition of cancer.
  arXiv  Detail & Related papers  (2024-05-05T21:08:49Z)
• TransRUPNet for Improved Polyp Segmentation [1.2498887792836635]
  We develop an advanced deep learning-based architecture, Transformer based Residual Upsampling Network (TransRUPNet) for automatic and real-time polyp segmentation. With the image size of $256times256$, the proposed method achieves an excellent real-time operation speed of 47.07 frames per second.
  arXiv  Detail & Related papers  (2023-06-03T19:06:06Z)
• RUPNet: Residual upsampling network for real-time polyp segmentation [2.6179759969345002]
  We propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation. With an image size of $512 times 512$, the proposed method achieves an excellent real-time operation speed of 152.60 frames per second.
  arXiv  Detail & Related papers  (2023-01-06T20:21:37Z)
• NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
  This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction. The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network. A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
  arXiv  Detail & Related papers  (2022-09-29T04:06:00Z)
• Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network [3.1374864575817214]
  In this study, we introduce a novel deep learning architecture, named textbfMKDCNet, for automatic polyp segmentation. Experiments on four publicly available polyp datasets and cell nuclei dataset show that the proposed MKDCNet outperforms the state-of-the-art methods. MKDCNet can be a strong benchmark for building real-time systems for clinical colonoscopies.
  arXiv  Detail & Related papers  (2022-06-13T15:47:38Z)
• Self-Supervised U-Net for Segmenting Flat and Sessile Polyps [63.62764375279861]
  Development of colorectal polyps is one of the earliest signs of cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Computer-Aided Diagnosis systems(CADx) has been proposed that detect polyps by processing the colonoscopic videos.
  arXiv  Detail & Related papers  (2021-10-17T09:31:20Z)
• Automatic Polyp Segmentation via Multi-scale Subtraction Network [100.94922587360871]
  In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. We propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image.
  arXiv  Detail & Related papers  (2021-08-11T07:54:07Z)
• Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation [82.61182037130406]
  This work deals with medical image segmentation and in particular with accurate polyp detection and segmentation during colonoscopy examinations. Basic architecture in image segmentation consists of an encoder and a decoder. We compare some variant of the DeepLab architecture obtained by varying the decoder backbone.
  arXiv  Detail & Related papers  (2021-04-02T02:07:37Z)
• 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)
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
  We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. We first aggregate the features in high-level layers using a parallel partial decoder (PPD) In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
  arXiv  Detail & Related papers  (2020-06-13T08:13:43Z)

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.