MixMicrobleedNet: segmentation of cerebral microbleeds using nnU-Net

TL;DR

This paper presents MixMicrobleedNet, a fully automated microbleed segmentation method using nnU-Net, achieving promising results on MICCAI 2021 challenge data without post-processing.

Contribution

It introduces the application of nnU-Net for microbleed segmentation in MRI, demonstrating its effectiveness without additional post-processing steps.

Findings

Estimated Dice score of 0.80 on training data

False discovery rate of 0.16, false negative rate of 0.15

Most false positives may be actual microbleeds missed in visual inspection

Abstract

Cerebral microbleeds are small hypointense lesions visible on magnetic resonance imaging (MRI) with gradient echo, T2*, or susceptibility weighted (SWI) imaging. Assessment of cerebral microbleeds is mostly performed by visual inspection. The past decade has seen the rise of semi-automatic tools to assist with rating and more recently fully automatic tools for microbleed detection. In this work, we explore the use of nnU-Net as a fully automated tool for microbleed segmentation. Data was provided by the ``Where is VALDO?'' challenge of MICCAI 2021. The final method consists of nnU-Net in the ``3D full resolution U-Net'' configuration trained on all data (fold = `all'). No post-processing options of nnU-Net were used. Self-evaluation on the training data showed an estimated Dice of 0.80, false discovery rate of 0.16, and false negative rate of 0.15. Final evaluation on the test set of the VALDO challenge is pending. Visual inspection of the results showed that most of the reported false positives could be an actual microbleed that might have been missed during visual rating. Source code is available at: https://github.com/hjkuijf/MixMicrobleedNet . The docker container hjkuijf/mixmicrobleednet can be pulled from https://hub.docker.com/r/hjkuijf/mixmicrobleednet .