# Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction

## TL;DR

This paper introduces a deep learning-based feature extraction method using inception models for vessel segmentation and centerline prediction, outperforming existing handcrafted features on public datasets.

## Contribution

It presents a novel multi-scale, multi-layer feature extraction scheme integrated with fully convolutional networks for biomedical image analysis.

## Key findings

- Achieved an average Dice score of 0.85 on DRIVE dataset.
- Outperformed most existing feature schemes in vessel segmentation.
- Extended the method to handle 3-D datasets.

## Abstract

Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on this feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and centerline detection and it out performs most existing hand crafted or deterministic feature schemes found in literature. We achieve an average maximum Dice of 0.85 on the DRIVE data set which out performs the scores from the second human annotator of this data set. We also achieve an average maximum Dice of 0.85 and kappa of 0.84 on the STARE data set. Though these datasets are mainly 2-D we also propose ways of extending this feature extraction scheme to handle 3-D datasets.