3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images

• URL: http://arxiv.org/abs/2003.07923v1
• Date: Tue, 17 Mar 2020 20:20:43 GMT
• Title: 3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images
• Authors: Cheryl Sital, Tom Brosch, Dominique Tio, Alexander Raaijmakers, J\"urgen Weese
• Abstract summary: 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.
• Score: 58.720142291102135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic segmentation of anatomical structures with convolutional neural networks (CNNs) constitutes a large portion of research in medical image analysis. The majority of CNN-based methods rely on an abundance of labeled data for proper training. Labeled medical data is often scarce, but unlabeled data is more widely available. This necessitates approaches that go beyond traditional supervised learning and leverage unlabeled data for segmentation tasks. This work investigates the potential of autoencoder-extracted features to improve segmentation with a CNN. Two strategies were considered. First, transfer learning where pretrained autoencoder features were used as initialization for the convolutional layers in the segmentation network. Second, multi-task learning where the tasks of segmentation and feature extraction, by means of input reconstruction, were learned and optimized simultaneously. 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. For both strategies, experiments were conducted with varying amounts of labeled and unlabeled training data. The proposed learning strategies improved results in $75\%$ of the experiments compared to training from scratch and increased the dice score by up to $0.040$ and $0.024$ for a ratio of unlabeled to labeled training data of about $32 : 1$ and $12.5 : 1$, respectively. The results indicate that both training strategies are more effective with a large ratio of unlabeled to labeled training data.

Related papers

• GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data [4.775846640214768]
  Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning. A key concept is that voxel features from labeled and unlabeled data close each other in the feature space more likely to belong to the same class. We introduce a Knowledge Transfer Cross Pseudo-label Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data.
  arXiv  Detail & Related papers  (2024-08-09T07:46:01Z)
• Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views [10.944692719150071]
  We propose a novel 3D brain segmentation approach using complementary 2D diffusion models. Our goal is to achieve reliable segmentation quality without requiring complete labels for each individual subject.
  arXiv  Detail & Related papers  (2024-07-17T06:14:53Z)
• Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation [9.93871075239635]
  We propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods.
  arXiv  Detail & Related papers  (2023-11-17T06:36:43Z)
• Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation [13.707121013895929]
  We present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation. We use distinct decoders for student and teacher networks while maintain the same encoder. To learn from unlabeled data, we create pseudo-labels generated by the teacher networks and augment the training data with the pseudo-labels.
  arXiv  Detail & Related papers  (2023-08-31T09:13:34Z)
• 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)
• 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)
• CvS: Classification via Segmentation For Small Datasets [52.821178654631254]
  This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps. We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples.
  arXiv  Detail & Related papers  (2021-10-29T18:41:15Z)
• Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation [61.09321488002978]
  We present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. Our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
  arXiv  Detail & Related papers  (2021-04-05T15:50:16Z)
• ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation [99.90263375737362]
  We propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
  arXiv  Detail & Related papers  (2020-06-24T04:05:12Z)

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.