Hessian-Based Lightweight Neural Network HessNet for State-of-the-Art Brain Vessel Segmentation on a Minimal Training Dataset

TL;DR

HessNet is a lightweight, Hessian-based neural network that achieves state-of-the-art brain vessel segmentation accuracy on minimal training data, enabling efficient annotation and dataset creation for medical imaging.

Contribution

The paper introduces HessNet, a semi-supervised, Hessian-based neural network with only 6000 parameters for 3D brain vessel segmentation, reducing resource needs and enabling high accuracy with minimal data.

Findings

HessNet achieves state-of-the-art segmentation accuracy on minimal datasets.

The neural network has only 6000 parameters, making it resource-efficient.

A large semi-annotated brain vessel dataset was created using HessNet.

Abstract

Accurate segmentation of blood vessels in brain magnetic resonance angiography (MRA) is essential for successful surgical procedures, such as aneurysm repair or bypass surgery. Currently, annotation is primarily performed through manual segmentation or classical methods, such as the Frangi filter, which often lack sufficient accuracy. Neural networks have emerged as powerful tools for medical image segmentation, but their development depends on well-annotated training datasets. However, there is a notable lack of publicly available MRA datasets with detailed brain vessel annotations. To address this gap, we propose a novel semi-supervised learning lightweight neural network with Hessian matrices on board for 3D segmentation of complex structures such as tubular structures, which we named HessNet. The solution is a Hessian-based neural network with only 6000 parameters. HessNet can run on the CPU and significantly reduces the resource requirements for training neural networks. The accuracy of vessel segmentation on a minimal training dataset reaches state-of-the-art results. It helps us create a large, semi-manually annotated brain vessel dataset of brain MRA images based on the IXI dataset (annotated 200 images). Annotation was performed by three experts under the supervision of three neurovascular surgeons after applying HessNet. It provides high accuracy of vessel segmentation and allows experts to focus only on the most complex important cases. The dataset is available at https://git.scinalytics.com/terilat/VesselDatasetPartly.