Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation

• URL: http://arxiv.org/abs/2011.10654v1
• Date: Fri, 20 Nov 2020 21:45:28 GMT
• Title: Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
• Authors: Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck and Andreas N\"urnberger
• Abstract summary: We propose an unsupervised deep learning based approach for automated segmentation. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. The proposed method has shown promising results, with a dice coefficient of 0.88 for the liver segmentation compared against manual segmentation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth. A possible solution for the problem would be an unsupervised deep learning based approach for automated segmentation, which this research work tries to address. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. In addition, to suppress noise in the segmentation we added attention gates to the skip connections. The loss for the segmentation output was calculated using soft N-Cuts and for the reconstruction output using SSIM. Conditional Random Fields were used as a post-processing step to fine-tune the results. The proposed method has shown promising results, with a dice coefficient of 0.88 for the liver segmentation compared against manual segmentation.

Related papers

• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors [1.376408511310322]
  We propose an approach to identify and measure erroneous regions in the segmentation map. Our method can estimate error at any point or node in a 3D mesh generated from a possibly erroneous volumetric segmentation map. We have evaluated our network on a high-resolution micro-CT dataset of the human inner-ear bony labyrinth structure.
  arXiv  Detail & Related papers  (2023-08-09T16:58:03Z)
• Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration [2.494736313545503]
  Traditional deep learning-based automatic segmentation requires extensive training data with ground-truth labels. We propose a novel method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image.
  arXiv  Detail & Related papers  (2023-07-07T13:17:12Z)
• 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)
• 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)
• Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks [2.554905387213585]
  The proposed method is composed of two steps: 1) generating pseudo masks with bounding box annotations by k-means clustering, and 2) iteratively training a 3D U-Net convolutional neural network as a segmentation model. For liver, spleen and kidney segmentation, it achieved an accuracy of 95.19%, 92.11%, and 91.45%, respectively.
  arXiv  Detail & Related papers  (2021-05-29T14:48:16Z)
• Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients [9.313053265087262]
  We propose a weakly-supervised segmentation method based on dense regression activation maps (dRAM) Our method substantially improves the intersection over union from 0.335 in the CAM-based weakly supervised segmentation method to 0.495.
  arXiv  Detail & Related papers  (2021-05-25T08:29:35Z)
• 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)
• Inter-slice Context Residual Learning for 3D Medical Image Segmentation [38.43650000401734]
  We propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We show that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation.
  arXiv  Detail & Related papers  (2020-11-28T16:03:39Z)
• Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound [74.22397862400177]
  We propose a novel Frustum ultrasound based catheter segmentation method. The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
  arXiv  Detail & Related papers  (2020-10-19T13:56:22Z)
• 3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images [58.720142291102135]
  This work investigates the potential of autoencoder-extracted features to improve segmentation with a convolutional neural network. A convolutional autoencoder was used to extract features from unlabeled data and a multi-scale, fully convolutional CNN was used to perform the target task of 3D liver segmentation in CT images.
  arXiv  Detail & Related papers  (2020-03-17T20:20:43Z)

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.