# RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation

arXiv: https://arxiv.org/abs/2001.09138 · HTML: https://mlsanity.com/paper/2001.09138/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Region-wise Loss for Biomedical Image Segmentation](https://mlsanity.com/paper/2108.01405/) (2021)
- [Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning](https://mlsanity.com/paper/2209.13590/) (2022)
- [FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation](https://mlsanity.com/paper/2210.15164/) (2022)
- [Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks](https://mlsanity.com/paper/2108.01941/) (2021)

## Abstract (arXiv)

We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2.