A deep learning model integrating FCNNs and CRFs for brain tumor segmentation (1702.04528v3)

Abstract: 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.

• The paper introduces a deep learning framework that combines FCNNs with CRF-RNNs to improve spatial consistency and segmentation accuracy in MRI scans.
• It employs a multi-stage training process across axial, coronal, and sagittal views using a voting-based fusion strategy for robust 3D segmentation.
• The approach achieved superior Dice coefficient and PPV scores, ranking first in BRATS 2016 and demonstrating enhanced computational efficiency.

Overview of the Paper on Brain Tumor Segmentation Using FCNNs and CRFs

The research paper presents a deep learning methodology for brain tumor segmentation by integrating Fully Convolutional Neural Networks (FCNNs) with Conditional Random Fields (CRFs) in a unified framework. This approach enhances the appearance and spatial consistency of segmentation outcomes, addressing the challenges associated with brain tumor identification in MRI scans.

Methodology

The integration is facilitated through a multi-stage training process:

1. FCNN Training: The model begins by training FCNNs using 2D image patches. This stage leverages the rich feature extraction capabilities of FCNNs to address the intrinsic variability in MRI data.
2. CRF-RNN Integration: Conditional Random Fields are formulated as Recurrent Neural Networks (CRF-RNNs). During this phase, image slices serve as training data to optimize segmentation consistency, keeping the FCNN parameters fixed.
3. Fine-tuning: The final stage involves fine-tuning both the FCNNs and CRF-RNN using image slices, allowing for an end-to-end optimization of the network.

The researchers employ three segmentation models, each trained on axial, coronal, and sagittal views, respectively, combining their outputs using a voting-based fusion strategy. This multi-view approach compensates for limitations inherent in single-plane analysis and offers a comprehensive 3D segmentation.

Experimental Evaluation

The authors evaluated their method using data from the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, 2015, and 2016. Notable results include:

• The proposed method achieved competitive performance with models using three modalities (Flair, T1c, T2), suggesting reduced data acquisition cost and complexity without sacrificing segmentation accuracy.
• Integration with CRF-RNN improved segmentation robustness against variations in patch size and number of training samples.
• A distinct preprocessing strategy utilizing robust deviation rather than standard deviation was applied, yielding slightly improved results.

Results and Implications

The results demonstrated that the FCNN+CRF framework improves both the Dice coefficient and Positive Predictive Value (PPV) while maintaining acceptable sensitivity levels, notably enhancing the spatial and appearance consistency of the segmentation outputs.

The proposed framework ranked first in the BRATS 2016 multi-temporal evaluation, underscoring the practical applicability and robustness of the approach. Moreover, the method presents a significant advantage in computational efficiency by performing slice-by-slice segmentation, which is faster than patch-based strategies.

Future Prospects

The paper opens avenues for further research into fully 3D networks, aiming to better utilize the 3D information present in MRI datasets. Future work may explore the extension of the integrated FCNN and CRF-RNN model to other medical imaging domains or the incorporation of new deep learning paradigms.

In conclusion, the integration of FCNNs and CRFs within this framework provides a promising direction for enhancing brain tumor segmentation accuracy and efficiency, with diverse implications for both clinical practice and future AI developments in medical imaging.