An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood Vessels

  title={An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood Vessels},
  author={Matheus V. da Silva and Julie Ouellette and Baptiste Lacoste and C{\'e}sar Henrique Comin},
  journal={Computer methods and programs in biomedicine},



Segmenting Retinal Blood Vessels With Deep Neural Networks

A supervised segmentation technique that uses a deep neural network trained on a large sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections, which significantly outperform the previous algorithms on the area under ROC curve measure.

Deep supervision with additional labels for retinal vessel segmentation task

This paper proposes a novel method with deep neural networks to solve the problem of segmenting vessels accurately, and introduces an edge-aware mechanism, in which the original task is converted into a multi-class task by adding additional labels on boundary areas.

Retinal blood vessel segmentation using fully convolutional network with transfer learning

Machine learning analysis of whole mouse brain vasculature

A deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP), which uses a convolutional neural network with a transfer learning approach for segmentation and achieves human-level accuracy.

DUNet: A deformable network for retinal vessel segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease

The U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorf distance (AVD) and revealed excellent performance in large vessels and sufficient performance in small vessels.

Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning

A generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning based vascular segmentation followed by graphing, that paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

The DeepVesselNet architecture does not use any form of sub-sampling layer and works well for vessel segmentation, centerline prediction, and bifurcation detection, and the results show that cross-hair filters achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy that does not differ from full 3-D filters.