A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation

• URL: http://arxiv.org/abs/2001.05829v1
• Date: Wed, 15 Jan 2020 17:06:10 GMT
• Title: A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation
• Authors: Shaoming Zheng, Tianyang Zhang, Jiawei Zhuang, Hao Wang, Jiang Liu
• Abstract summary: It is often difficult to obtain desirable segmentation performance on thin vessels and boundary areas. We propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem. Our model is proved to outperform state-of-the-art methods on DRIVE, STARE, and CHASE_DB1 datasets.
• Score: 11.469357649111076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vessel segmentation in fundus is a key diagnostic capability in ophthalmology, and there are various challenges remained in this essential task. Early approaches indicate that it is often difficult to obtain desirable segmentation performance on thin vessels and boundary areas due to the imbalance of vessel pixels with different thickness levels. In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem. To pay more attention to the thin vessels and boundary areas, we firstly propose an efficient hierarchical model automatically stratifies the ground-truth masks into different thickness levels. Then a novel two-stream adversarial network is introduced to use the stratification results with a balanced loss function and an integration operation to achieve a better performance, especially in thin vessels and boundary areas detecting. Our model is proved to outperform state-of-the-art methods on DRIVE, STARE, and CHASE_DB1 datasets.

Related papers

• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images [50.76668288066681]
  We present a scale-aware super-resolution network to adaptively segment lesions of various sizes from low-resolution medical images. Our proposed network achieved consistent improvements compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-30T14:25:55Z)
• DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization [6.278444803136043]
  We propose a novel transformer-based architecture called DualStreamFoveaNet (DSFN) for multi-cue fusion. This architecture explicitly incorporates long-range connections and global features using retina and vessel distributions for robust fovea localization. We demonstrate that the DSFN is more robust on both normal and diseased retina images and has better capacity generalization in cross-dataset experiments.
  arXiv  Detail & Related papers  (2023-02-14T10:40:20Z)
• Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module [14.021944194533644]
  Hard exudates (HE) is the most specific biomarker for retina edema. This paper proposes a novel hard exudates segmentation method named SS-MAF with an auxiliary super-resolution task. We evaluate our method on two public lesion datasets, IDRiD and E-Ophtha.
  arXiv  Detail & Related papers  (2022-11-17T08:25:04Z)
• RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation [3.57686754209902]
  Quantification of retinal fluids is necessary for OCT-guided treatment management. New convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. Model benefits from hierarchical representation learning of textural, contextual, and edge features.
  arXiv  Detail & Related papers  (2022-09-26T07:18:00Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Dispensed Transformer Network for Unsupervised Domain Adaptation [21.256375606219073]
  A novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our proposed network achieves the best performance in comparison with several state-of-the-art techniques.
  arXiv  Detail & Related papers  (2021-10-28T08:27:44Z)
• Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition [49.163326827954656]
  We propose a novel multi-granular-temporal graph network for skeleton-based action classification. We develop a dual-head graph network consisting of two inter-leaved branches, which enables us to extract at least two-temporal resolutions. We conduct extensive experiments on three large-scale datasets.
  arXiv  Detail & Related papers  (2021-08-10T09:25:07Z)
• Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation [58.45470500617549]
  We propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images.
  arXiv  Detail & Related papers  (2021-05-31T06:55:43Z)
• Detecting micro fractures with X-ray computed tomography [4.855026133182103]
  We present a data-set produced by the successful visualization of a fracture network in Carrara marble with XRCT. Three conventional and two machine-learning-based methods are evaluated. The output of the 2D U-net model is one of the adopted machine-learning-based segmentation methods.
  arXiv  Detail & Related papers  (2021-03-23T20:20:24Z)
• Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
  We propose a novel learning scheme for unpaired cross-modality image segmentation. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI. We have extensively validated our approach on two multi-class segmentation problems.
  arXiv  Detail & Related papers  (2020-01-06T20:03:17Z)

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.