Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

• URL: http://arxiv.org/abs/2007.10732v1
• Date: Tue, 21 Jul 2020 11:44:52 GMT
• Title: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
• Authors: Shuailin Li, Chuyu Zhang, and Xuming He
• Abstract summary: We propose a shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. We develop a multi-task deep network that jointly predicts semantic segmentation and signed distance mapDM) of object surfaces. Experiments show that our method outperforms current state-of-the-art approaches with improved shape estimation.
• Score: 24.216869988183092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods. Most existing semi-supervised segmentation approaches either tend to neglect geometric constraint in object segments, leading to incomplete object coverage, or impose strong shape prior that requires extra alignment. In this work, we propose a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. To achieve this, we develop a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces. During training, we introduce an adversarial loss between the predicted SDMs of labeled and unlabeled data so that our network is able to capture shape-aware features more effectively. Experiments on the Atrial Segmentation Challenge dataset show that our method outperforms current state-of-the-art approaches with improved shape estimation, which validates its efficacy. Code is available at https://github.com/kleinzcy/SASSnet.

Related papers

• Bayesian Self-Training for Semi-Supervised 3D Segmentation [59.544558398992386]
  3D segmentation is a core problem in computer vision. densely labeling 3D point clouds to employ fully-supervised training remains too labor intensive and expensive. Semi-supervised training provides a more practical alternative, where only a small set of labeled data is given, accompanied by a larger unlabeled set.
  arXiv  Detail & Related papers  (2024-09-12T14:54:31Z)
• 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)
• Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors [3.774643767869751]
  We propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation. To achieve this, we augment a 2.5D attention UNet with a proposed label propagation module to extend semantic information from scribbles. Also, we propose an optional add-on component, which incorporates the shape prior information from unpaired segmentation masks to further improve model accuracy.
  arXiv  Detail & Related papers  (2023-10-12T07:17:14Z)
• Self-supervised Semantic Segmentation: Consistency over Transformation [3.485615723221064]
  We propose a novel self-supervised algorithm, textbfS$3$-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. We leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.
  arXiv  Detail & Related papers  (2023-08-31T21:28:46Z)
• Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization [98.46318529630109]
  We take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. By clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions.
  arXiv  Detail & Related papers  (2022-05-16T17:47:44Z)
• Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training [26.128597729527208]
  In this paper, a novel geometry-aware semi-supervised learning framework is proposed for medical image segmentation. Our framework outperforms six state-of-the-art semi-supervised segmentation methods.
  arXiv  Detail & Related papers  (2022-02-12T17:07:17Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization [7.165364364478119]
  We propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adrial Regularization (InMARS) Inspired by human perception which parses a scene into perceptual groups, our proposed approach first partitions an input image into meaningful regions (also known as superpixels) Next, it utilizes Mutual-Information-Maximization followed by an adversarial training strategy to cluster these regions into semantically meaningful classes. Our experiments demonstrate that our method achieves the state-of-the-art performance on two commonly used unsupervised semantic segmentation datasets.
  arXiv  Detail & Related papers  (2021-07-01T18:36:27Z)
• Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model [27.013224147257198]
  We propose a novel weakly-supervised segmentation strategy capable of better capturing 3D shape prior in both model prediction and learning. Our main idea is to extract a self-taught shape representation by leveraging weak labels, and then integrate this representation into segmentation prediction for shape refinement.
  arXiv  Detail & Related papers  (2021-04-27T10:03:45Z)
• SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images [94.36401543589523]
  We introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks. We then propose a Semantic Object and Depth Estimation Network (SOSD-Net) based on the objectness assumption. To the best of our knowledge, SOSD-Net is the first network that exploits the geometry constraint for simultaneous monocular depth estimation and semantic segmentation.
  arXiv  Detail & Related papers  (2021-01-19T02:41:03Z)
• Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation [90.87105131054419]
  We present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains.
  arXiv  Detail & Related papers  (2020-12-19T21:18:03Z)

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.