Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2410.10287v1
• Date: Mon, 14 Oct 2024 08:40:35 GMT
• Title: Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentation
• Authors: Sicheng Shen, Jinming Cao, Yifang Yin, Roger Zimmermann,
• Abstract summary: We introduce the Manifold-Aware Local Feature Modeling Network (MANet), which enhances the U-Net architecture by incorporating manifold supervision signals. Our experiments on datasets such as ACDC, LA, and Pancreas-NIH demonstrate that MANet consistently surpasses state-of-the-art methods in performance metrics.
• Score: 20.69908466577971
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Achieving precise medical image segmentation is vital for effective treatment planning and accurate disease diagnosis. Traditional fully-supervised deep learning methods, though highly precise, are heavily reliant on large volumes of labeled data, which are often difficult to obtain due to the expertise required for medical annotations. This has led to the rise of semi-supervised learning approaches that utilize both labeled and unlabeled data to mitigate the label scarcity issue. In this paper, we introduce the Manifold-Aware Local Feature Modeling Network (MANet), which enhances the U-Net architecture by incorporating manifold supervision signals. This approach focuses on improving boundary accuracy, which is crucial for reliable medical diagnosis. To further extend the versatility of our method, we propose two variants: MA-Sobel and MA-Canny. The MA-Sobel variant employs the Sobel operator, which is effective for both 2D and 3D data, while the MA-Canny variant utilizes the Canny operator, specifically designed for 2D images, to refine boundary detection. These variants allow our method to adapt to various medical image modalities and dimensionalities, ensuring broader applicability. Our extensive experiments on datasets such as ACDC, LA, and Pancreas-NIH demonstrate that MANet consistently surpasses state-of-the-art methods in performance metrics like Dice and Jaccard scores. The proposed method also shows improved generalization across various semi-supervised segmentation networks, highlighting its robustness and effectiveness. Visual analysis of segmentation results confirms that MANet offers clearer and more accurate class boundaries, underscoring the value of manifold information in medical image segmentation.

Related papers

• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• 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-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model [0.0]
  We propose a framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
  arXiv  Detail & Related papers  (2023-05-16T14:08:24Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Analysing the effectiveness of a generative model for semi-supervised medical image segmentation [23.898954721893855]
  State-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. Semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models. Deep generative models such as the SemanticGAN are truly viable alternatives to tackle challenging medical image segmentation problems.
  arXiv  Detail & Related papers  (2022-11-03T15:19:59Z)
• CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation [8.307551496968156]
  We present CUTS, an unsupervised deep learning framework for medical image segmentation. For each image, it produces an embedding map via intra-image contrastive learning and local patch reconstruction. CUTS yields a series of coarse-to-fine-grained segmentations that highlight features at various granularities.
  arXiv  Detail & Related papers  (2022-09-23T01:09:06Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation [71.89867233426597]
  In this paper, we aim to exploit the prior knowledge learned from one modality to improve the segmentation performance on another modality. We propose a novel Mutual Knowledge Distillation scheme to thoroughly exploit the modality-shared knowledge. Experimental results on the public multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method achieves large improvements on CT segmentation.
  arXiv  Detail & Related papers  (2020-10-04T10:25:13Z)
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation [35.33425093398756]
  Unlabeled data is much easier to acquire than well-annotated data. We propose uncertainty-aware multi-view co-training for medical image segmentation. Our framework is capable of efficiently utilizing unlabeled data for better performance.
  arXiv  Detail & Related papers  (2020-06-28T22:04:54Z)

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.