Automated ischemic stroke lesion segmentation from 3D MRI

Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs. In this work, we describe our solution to ISLES 2022 segmentation task. We re-sample all images to a common resolution, use two input MRI modalities (DWI and ADC) and train SegResNet semantic segmentation network from MONAI. The final submission is an ensemble of 15 models (from 3 runs of 5-fold cross validation). Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.824), and overall rank 2 (based on the combined metric ranking).

READ FULL TEXT
research
09/22/2022

Automated head and neck tumor segmentation from 3D PET/CT

Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platf...
research
09/21/2022

Automated segmentation of intracranial hemorrhages from 3D CT

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a ...
research
08/23/2022

Extending nnU-Net is all you need

Semantic segmentation is one of the most popular research areas in medic...
research
08/15/2019

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

Automatic segmentation methods based on deep learning have recently demo...
research
07/18/2018

Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification

An approach to lesion recognition is described that for lesion localizat...
research
12/13/2021

5th Place Solution for VSPW 2021 Challenge

In this article, we introduce the solution we used in the VSPW 2021 Chal...

Please sign up or login with your details

Forgot password? Click here to reset