# Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1709.00382 · HTML: https://mlsanity.com/paper/1709.00382/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2017.

## Built on by (most cited first, 30 of 33)

- [Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2104.06231/) (2021)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Cross-Modality Deep Feature Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2201.02356/) (2022)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [A Tri-attention Fusion Guided Multi-modal Segmentation Network](https://mlsanity.com/paper/2111.01623/) (2021)
- [Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss](https://mlsanity.com/paper/2102.01897/) (2021)
- [Trinity: A No-Code AI platform for complex spatial datasets](https://mlsanity.com/paper/2106.11756/) (2021)
- [3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework](https://mlsanity.com/paper/2107.12046/) (2021)
- [Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma](https://mlsanity.com/paper/2208.04680/) (2022)
- [3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint](https://mlsanity.com/paper/2102.03111/) (2021)
- [A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2202.06344/) (2022)
- [Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2302.02521/) (2023)
- [Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection](https://mlsanity.com/paper/2403.12748/) (2024)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [Brain tumour segmentation using a triplanar ensemble of U-Nets](https://mlsanity.com/paper/2105.11356/) (2021)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages](https://mlsanity.com/paper/2103.04838/) (2021)
- [A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors](https://mlsanity.com/paper/2109.14775/) (2021)
- [One-shot Weakly-Supervised Segmentation in Medical Images](https://mlsanity.com/paper/2111.10773/) (2021)
- [Residual Channel Attention Network for Brain Glioma Segmentation](https://mlsanity.com/paper/2205.10758/) (2022)
- [Deep Learning for Human Parsing: A Survey](https://mlsanity.com/paper/2301.12416/) (2023)
- [Bladder segmentation based on deep learning approaches: current limitations and lessons](https://mlsanity.com/paper/2101.06498/) (2021)
- [Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs](https://mlsanity.com/paper/2210.14409/) (2022)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)
- [Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism](https://mlsanity.com/paper/2304.08072/) (2023)
- [Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas](https://mlsanity.com/paper/2502.19760/) (2025)
- [BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation](https://mlsanity.com/paper/2505.06133/) (2025)
- [Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN](https://mlsanity.com/paper/2509.05821/) (2025)
- [NeuroVascU-Net: A Unified Multi-Scale and Cross-Domain Adaptive Feature Fusion U-Net for Precise 3D Segmentation of Brain Vessels in Contrast-Enhanced T1 MRI](https://mlsanity.com/paper/2511.18422/) (2025)

## Abstract (arXiv)

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.