# A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2307.08048 · HTML: https://mlsanity.com/paper/2307.08048/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 69 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation](https://mlsanity.com/paper/1711.01468/) (2017)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation](https://mlsanity.com/paper/1810.07884/) (2018)
- [A deep learning model integrating FCNNs and CRFs for brain tumor segmentation](https://mlsanity.com/paper/1702.04528/) (2017)
- [Deep Learning Based Brain Tumor Segmentation: A Survey](https://mlsanity.com/paper/2007.09479/) (2020)
- [3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI](https://mlsanity.com/paper/1904.03355/) (2019)
- [Cascaded V-Net using ROI masks for brain tumor segmentation](https://mlsanity.com/paper/1812.11588/) (2018)
- [Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution](https://mlsanity.com/paper/2011.01045/) (2020)
- [MRI Tumor Segmentation with Densely Connected 3D CNN](https://mlsanity.com/paper/1802.02427/) (2018)
- [Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning](https://mlsanity.com/paper/2109.05664/) (2021)
- [A Tri-attention Fusion Guided Multi-modal Segmentation Network](https://mlsanity.com/paper/2111.01623/) (2021)
- [3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework](https://mlsanity.com/paper/2107.12046/) (2021)
- [Prior Attention Network for Multi-Lesion Segmentation in Medical Images](https://mlsanity.com/paper/2110.04735/) (2021)
- [3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context](https://mlsanity.com/paper/1807.08599/) (2018)
- [Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI](https://mlsanity.com/paper/2004.14462/) (2020)
- [Category Guided Attention Network for Brain Tumor Segmentation in MRI](https://mlsanity.com/paper/2203.15383/) (2022)
- [Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI](https://mlsanity.com/paper/1806.02318/) (2018)
- [Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans](https://mlsanity.com/paper/2101.12404/) (2021)

## Abstract (arXiv)

Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Biomedical image analysis is a widely known solution to diagnose brain tumor. Although MRI is the current standard method for imaging tumors, its clinical usefulness is constrained by the requirement of manual segmentation which is time-consuming. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools and the UNet architecture is commonly used for segmentation. However, the traditional UNet has limitations in terms of complexity, training, accuracy, and contextual information processing. As a result, the modified UNet architecture, which incorporates residual dense blocks, layered attention, and channel attention modules, in addition to stacked convolution, can effectively capture both coarse and fine feature information. The proposed SLCA UNet approach achieves good performance on the freely accessible Brain Tumor Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845, 0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for BraTS 2020 dataset, respectively.