# A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

arXiv: https://arxiv.org/abs/1702.04528 · HTML: https://mlsanity.com/paper/1702.04528/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2017.

## Built on by (most cited first, 30 of 35)

- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.07370/) (2022)
- [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation](https://mlsanity.com/paper/2311.10696/) (2023)
- [A Tri-attention Fusion Guided Multi-modal Segmentation Network](https://mlsanity.com/paper/2111.01623/) (2021)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms](https://mlsanity.com/paper/2101.07715/) (2021)
- [3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework](https://mlsanity.com/paper/2107.12046/) (2021)
- [PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images](https://mlsanity.com/paper/2103.00274/) (2021)
- [H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation](https://mlsanity.com/paper/2307.01486/) (2023)
- [Trichomonas Vaginalis Segmentation in Microscope Images](https://mlsanity.com/paper/2207.00973/) (2022)
- [BlindHarmony: "Blind" Harmonization for MR Images via Flow model](https://mlsanity.com/paper/2305.10732/) (2023)
- [HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images](https://mlsanity.com/paper/2101.02877/) (2021)
- [ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation](https://mlsanity.com/paper/2203.11213/) (2022)
- [A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.14134/) (2022)
- [Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2307.11604/) (2023)
- [LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models](https://mlsanity.com/paper/2409.19540/) (2024)
- [TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration](https://mlsanity.com/paper/2506.16784/) (2025)
- [3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint](https://mlsanity.com/paper/2102.03111/) (2021)
- [Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients](https://mlsanity.com/paper/2209.01402/) (2022)
- [Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection](https://mlsanity.com/paper/2210.06512/) (2022)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [Trust, but Verify: Robust Image Segmentation using Deep Learning](https://mlsanity.com/paper/2310.16999/) (2023)
- [DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models](https://mlsanity.com/paper/2202.11841/) (2022)
- [Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks](https://mlsanity.com/paper/2206.06730/) (2022)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)

## Abstract (arXiv)

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.