Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation

• URL: http://arxiv.org/abs/2211.06578v2
• Date: Wed, 10 May 2023 10:23:37 GMT
• Title: Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
• Authors: Tianyi Shi, Xiaohuan Ding, Wei Zhou, Feng Pan, Zengqiang Yan, Xiang Bai and Xin Yang
• Abstract summary: Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
• Score: 48.638327652506284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various contrast variations are critical and challenging, and most existing methods focus only on achieving one or two of these aspects. In this paper, we present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach. Specifically, we compute a multiscale affinity field for each pixel, capturing its semantic relationships with neighboring pixels in the predicted mask image. This field represents the local geometry of vessel segments of different sizes, allowing us to learn spatial- and scale-aware adaptive weights to strengthen vessel features. We evaluate our AFN on four different types of vascular datasets: X-ray angiography coronary vessel dataset (XCAD), portal vein dataset (PV), digital subtraction angiography cerebrovascular vessel dataset (DSA) and retinal vessel dataset (DRIVE). Extensive experimental results demonstrate that our AFN outperforms the state-of-the-art methods in terms of both higher accuracy and topological metrics, while also being more robust to various contrast changes. The source code of this work is available at https://github.com/TY-Shi/AFN.

Related papers

• TransUNext: towards a more advanced U-shaped framework for automatic vessel segmentation in the fundus image [19.16680702780529]
  We propose a more advanced U-shaped architecture for a hybrid Transformer and CNN: TransUNext. The Global Multi-Scale Fusion (GMSF) module is further introduced to upgrade skip-connections, fuse high-level semantic and low-level detailed information, and eliminate high- and low-level semantic differences.
  arXiv  Detail & Related papers  (2024-11-05T01:44:22Z)
• 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)
• Deep Angiogram: Trivializing Retinal Vessel Segmentation [1.8479315677380455]
  We propose a contrastive variational auto-encoder that can filter out irrelevant features and synthesize a latent image, named deep angiogram. The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
  arXiv  Detail & Related papers  (2023-07-01T06:13:10Z)
• Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations [12.571349114534597]
  We present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets.
  arXiv  Detail & Related papers  (2023-06-19T14:01:47Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors [4.2663199451998475]
  We introduce a semi-supervised paradigm into the retinal layer segmentation task. In particular, a novel fully differentiable approach is used for converting surface position regression into a pixel-wise structured segmentation. In parallel, we propose a set of anatomical priors to improve network training when a limited amount of labeled data is available.
  arXiv  Detail & Related papers  (2022-07-01T14:30:59Z)
• 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)
• (M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation [0.0]
  We propose a multi-scale, multi-level attention embedded CNN architecture ((M)SLAe-Net) to address the issue of multi-stage processing. We do this by extracting features at multiple scales and multiple levels of the network, enabling our model to holistically extracts the local and global features. Our unique network design and novel D-DPP module with efficient task-specific loss function for thin vessels enabled our model for better cross data performance.
  arXiv  Detail & Related papers  (2021-09-05T14:29:00Z)
• Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation [53.187152856583396]
  We propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features.
  arXiv  Detail & Related papers  (2020-10-09T08:22:54Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.