Selective Information Passing for MR/CT Image Segmentation

• URL: http://arxiv.org/abs/2010.04920v1
• Date: Sat, 10 Oct 2020 06:47:53 GMT
• Title: Selective Information Passing for MR/CT Image Segmentation
• Authors: Qikui Zhu, Liang Li, Jiangnan Hao, Yunfei Zha, Yan Zhang, Yanxiang Cheng, Fei Liao, Pingxiang Li
• Abstract summary: We propose a novel 3D network with self-supervised function, named selective information passing network (SIP-Net) We evaluate our proposed model on the MICCAI MR Image 2012 Grant Challenge dataset, TCIA Pancreas CT-82-82 and MICCAI 2017 Prostate Liver Tumor (LiTS) Challenge dataset.
• Score: 9.898316422853528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between images. Many researchers proposed an encoder-decoder architecture with skip connections to combine low-level feature maps from the encoder path with high-level feature maps from the decoder path for automatically segmenting medical images. The skip connections have been shown to be effective in recovering fine-grained details of the target objects and may facilitate the gradient back-propagation. However, not all the feature maps transmitted by those connections contribute positively to the network performance. In this paper, to adaptively select useful information to pass through those skip connections, we propose a novel 3D network with self-supervised function, named selective information passing network (SIP-Net). We evaluate our proposed model on the MICCAI Prostate MR Image Segmentation 2012 Grant Challenge dataset, TCIA Pancreas CT-82 and MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset. The experimental results across these data sets show that our model achieved improved segmentation results and outperformed other state-of-the-art methods. The source code of this work is available at https://github.com/ahukui/SIPNet.

Related papers

• TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting [6.987177704136503]
  High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method. Most of the existing deep learning-based techniques for medical image segmentation are optimized for input images having small spatial dimensions and perform poorly on high-resolution images. We propose a parallel-in-branch architecture called TransResNet, which incorporates Transformer and CNN in a parallel manner to extract features from multi-resolution images independently.
  arXiv  Detail & Related papers  (2024-10-01T18:22:34Z)
• AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net [0.0]
  This paper proposes an attention-based residual Double U-Net architecture (AttResDU-Net) that improves on the existing medical image segmentation networks. We conducted experiments on three datasets: CVC Clinic-DB, ISIC 2018, and the 2018 Data Science Bowl datasets and achieved Dice Coefficient scores of 94.35%, 91.68%, and 92.45% respectively.
  arXiv  Detail & Related papers  (2023-06-25T14:28:08Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images [0.696194614504832]
  Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. We proposed an generalization-Decoder Network, Quick Attention Module and a Multi Loss Function. We evaluate the capability of our proposed network on two publicly available datasets for medical image segmentation MoNuSeg and GlaS.
  arXiv  Detail & Related papers  (2022-09-01T21:10:00Z)
• MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet [55.16833099336073]
  We propose to self-distill a Transformer-based UNet for medical image segmentation. It simultaneously learns global semantic information and local spatial-detailed features. Our MISSU achieves the best performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-02T07:38:53Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images [1.8146155083014204]
  This paper proposes a novel algorithm for automated vertebrae segmentation via 3D volumetric spine CT images. The proposed model is based on the structure of encoder to decoder, using layer normalization to optimize mini-batch training performance. The experimental results show that our model achieves competitive performance compared with other state-of-the-art medical semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-04-08T12:09:16Z)
• 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)
• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation [95.51455777713092]
  Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. We propose a novel framework that efficiently bridges a bf Convolutional neural network and a bf Transformer bf (CoTr) for accurate 3D medical image segmentation.
  arXiv  Detail & Related papers  (2021-03-04T13:34:22Z)
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [78.01570371790669]
  Medical image segmentation is an essential prerequisite for developing healthcare systems. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard. We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
  arXiv  Detail & Related papers  (2021-02-08T16:10:50Z)
• Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation [32.96604621259756]
  We propose an extension of U-Net for medical image segmentation. We take full advantages of U-Net, Squeeze and Excitation (SE) block, bi-directional ConvLSTM (BConvLSTM), and the mechanism of dense convolutions. The proposed model is evaluated on six datasets.
  arXiv  Detail & Related papers  (2020-03-10T12:29:59Z)

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.