VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation

• URL: http://arxiv.org/abs/2203.06425v1
• Date: Sat, 12 Mar 2022 12:40:20 GMT
• Title: VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
• Authors: Yukun Zhou, Moucheng Xu, Yipeng Hu, Stefano B. Blumberg, An Zhao, Siegfried K. Wagner, Pearse A. Keane, and Daniel C. Alexander
• Abstract summary: Two common vascular features, vessel density and fractal dimension, are identified to be sensitive to intra-segment misclassification. We show that incorporating our end-to-end VAFO-Loss in standard segmentation networks indeed improves vascular feature estimation. We also report a technically interesting finding that the trained segmentation network, albeit biased by the feature optimised loss VAFO-Loss, shows statistically significant improvement in segmentation metrics.
• Score: 7.670940374203646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease. In this work, we integrate these clinical features into a novel vascular feature optimised loss function (VAFO-Loss), in order to regularise networks to produce segmentation maps, with which more accurate vascular features can be derived. Two common vascular features, vessel density and fractal dimension, are identified to be sensitive to intra-segment misclassification, which is a well-recognised problem in multi-class artery/vein segmentation particularly hindering the estimation of these vascular features. Thus we encode these two features into VAFO-Loss. We first show that incorporating our end-to-end VAFO-Loss in standard segmentation networks indeed improves vascular feature estimation, yielding quantitative improvement in stroke incidence prediction, a clinical downstream task. We also report a technically interesting finding that the trained segmentation network, albeit biased by the feature optimised loss VAFO-Loss, shows statistically significant improvement in segmentation metrics, compared to those trained with other state-of-the-art segmentation losses.

Related papers

• Region Guided Attention Network for Retinal Vessel Segmentation [19.587662416331682]
  We present a lightweight retinal vessel segmentation network based on the encoder-decoder mechanism with region-guided attention. Dice loss penalises false positives and false negatives equally, encouraging the model to generate more accurate segmentation. Experiments on a benchmark dataset show better performance (0.8285, 0.8098, 0.9677, and 0.8166 recall, precision, accuracy and F1 score respectively) compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-22T00:08:18Z)
• RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation [58.618797429661754]
  We propose a rotation-and-scale equivariant Fourier parameterized convolution (RSF-Conv) specifically for retinal vessel segmentation. As a general module, RSF-Conv can be integrated into existing networks in a plug-and-play manner. To demonstrate the effectiveness of RSF-Conv, we also apply RSF-Conv+U-Net and RSF-Conv+Iter-Net to retinal artery/vein classification.
  arXiv  Detail & Related papers  (2023-09-27T13:14:57Z)
• MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation [1.3295074739915493]
  We propose a multiple attention-guided fusion network (MAF-Net) to accurately detect blood vessels in retinal fundus images. Traditional UNet-based models may lose partial information due to explicitly modeling long-distance dependencies. We show that our method produces satisfactory results compared to some state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-05T15:22:20Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning [0.0]
  Deconvolution methods are used to obtain clinically interpretable perfusion parameters. AIFNet is a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. We conclude that AIFNet has potential for clinical transfer and could be incorporated in deconvolution software.
  arXiv  Detail & Related papers  (2020-10-04T16:14:45Z)
• Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification [49.64863177155927]
  We propose a multi-task deep neural network with spatial activation mechanism to segment full retinal vessel, artery and vein simultaneously. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks.
  arXiv  Detail & Related papers  (2020-07-18T05:46:47Z)
• Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing [27.825969553813092]
  Vessel segmentation and artery/vein classification provide various information on potential disorders. We adopt a UNet-based model, SeqNet, to accurately segment vessels from the background and make prediction on the vessel type. Our experiments show that our method improves AUC to 0.98 for segmentation and the accuracy to 0.92 in classification over DRIVE dataset.
  arXiv  Detail & Related papers  (2020-05-27T13:06:16Z)
• Neural Network Segmentation of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis in Renal Biopsies [0.0]
  Glomerulosclerosis, interstitial fibrosis, and tubular atrophy (IFTA) are histologic indicators of irrecoverable kidney injury. Modern artificial intelligence and computer vision algorithms have the ability to reduce inter-observer variability through rigorous quantitation. We apply convolutional neural networks for the segmentation of glomerulosclerosis and IFTA in periodic acid-Schiff stained renal biopsies.
  arXiv  Detail & Related papers  (2020-02-28T17:05:59Z)

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.