E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

• URL: http://arxiv.org/abs/2007.09791v1
• Date: Sun, 19 Jul 2020 21:50:22 GMT
• Title: E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
• Authors: Youbao Tang, Yuxing Tang, Yingying Zhu, Jing Xiao and Ronald M. Summers
• Abstract summary: We propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, the second stage is an edge enhanced network (E$2$Net) E$2$Net explicitly models complementary objects (liver and tumor) and their edge information within the network to preserve the organ and lesion boundaries. The proposed framework has shown superior performance on both liver and liver tumor segmentation compared to several state-of-the-art 2D, 3D and 2D/3D hybrid frameworks.
• Score: 19.8618638586525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, while the second stage is an edge enhanced network (E$^2$Net) for more accurate liver and tumor segmentation. E$^2$Net explicitly models complementary objects (liver and tumor) and their edge information within the network to preserve the organ and lesion boundaries. We introduce an edge prediction module in E$^2$Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network. We also propose a deep cross feature fusion module to refine multi-scale features from both objects and their edges. E$^2$Net is more easily and efficiently trained with a small labeled dataset, and it can be trained/tested on the original 2D CT slices (resolve resampling error issue in 3D models). The proposed framework has shown superior performance on both liver and liver tumor segmentation compared to several state-of-the-art 2D, 3D and 2D/3D hybrid frameworks.

Related papers

• YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT) [11.779888288866577]
  Liver tumors are abnormal growths in the liver that can be benign or malignant. There is no dataset for plain scan segmentation of liver tumors. We propose Plain Scan Liver Tumors(PSLT) and YNetr.
  arXiv  Detail & Related papers  (2024-03-30T11:41:19Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations [1.7413461132662074]
  We propose a novel model, denoted a multi-dimension unified Swin transformer (MDU-ST) for 3D lesion segmentation. The network's performance is evaluated by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D lesion dataset. The proposed method can be used to conduct automated 3D lesion segmentation to assist radiomics and tumor growth modeling studies.
  arXiv  Detail & Related papers  (2023-09-04T21:24:00Z)
• Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19 [76.51091445670596]
  Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19. Most current COVID-19 infection segmentation methods mainly relied on 2D CT images, which lack 3D sequential constraint. Existing 3D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3D volume.
  arXiv  Detail & Related papers  (2022-11-10T13:11:21Z)
• View-Disentangled Transformer for Brain Lesion Detection [50.4918615815066]
  We propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions.
  arXiv  Detail & Related papers  (2022-09-20T11:58:23Z)
• Efficient liver segmentation with 3D CNN using computed tomography scans [0.0]
  Liver diseases due to liver tumors are one of the most common reasons around the globe. Many imaging modalities can be used as aiding tools to detect liver tumors. This paper proposed an efficient automatic liver segmentation framework to detect and segment the liver out of CT abdomen scans.
  arXiv  Detail & Related papers  (2022-08-28T19:02:39Z)
• 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)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Fusion Encoder: Application to Liver Tumor and Vessel 3D reconstruction [19.048952361722876]
  We introduce a multi-scale feature context fusion network called TransFusionNet based on Transformer and SEBottleNet. Experiments show that TransFusionNet is better than the state-of-the-art method on both the public dataset LITS and 3Dircadb and our clinical dataset. We also propose an automatic 3D reconstruction algorithm based on the trained model.
  arXiv  Detail & Related papers  (2021-11-26T02:48:48Z)
• A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images [31.245109574064532]
  We propose a novel hybrid CNN that fuses 2D and 3D convolutions to combat the different spatial resolutions and extract effective edge and semantic features from 3D HaN CT images. Experiments on the MICCAI 2015 challenge dataset demonstrate that OrganNet2.5D achieves promising performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-26T15:37:47Z)
• Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples [74.92488215859991]
  We propose a novel 3D-based coarse-to-fine framework to efficiently tackle these challenges. The proposed 3D-based framework outperforms their 2D counterparts by a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on three datasets, the NIH pancreas dataset, the JHMI pancreas dataset and the JHMI pathological cyst dataset.
  arXiv  Detail & Related papers  (2020-10-29T15:39:19Z)

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.