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Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo
TL;DR
Manual brain tumor extent segmentation from MRI is time-consuming and operator-dependent, motivating reliable automatic methods. The paper develops a U-Net-based fully convolutional network with augmentation and Soft Dice loss, evaluated on BRATS 2015 HGG and LGG datasets. Cross-validation reports promising, efficient segmentation, including comparable complete-tumor and superior core-tumor results relative to other state-of-the-art methods.
Problem
Manual MRI tumor segmentation is time-consuming, subjective, and difficult to reproduce, while tumor heterogeneity makes automatic delineation challenging.
Method
A 2D U-Net-based fully convolutional network combines comprehensive data augmentation with Soft Dice loss for automatic brain tumor segmentation.
Results
Cross-validation on BRATS 2015 showed comparable complete-tumor and superior core-tumor segmentation versus other state-of-the-art methods.
Takeaways & Limitations
The method provides efficient and robust automatic segmentation across HGG and LGG cases within the evaluated BRATS 2015 setting.
Takeaways & Limitations
Evaluation used five-fold cross-validation rather than a separate independent testing dataset, and broader multi-institutional and longitudinal validation remains needed.
Abstract
from arXiv · showhide
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.
1 Introduction
Brain tumor MRI segmentation is clinically important but remains difficult and labor-intensive because tumors vary substantially and manual delineation is operator-dependent. The paper motivates a fully automatic U-Net-based approach to improve segmentation efficiency and accuracy.
- Clinical motivation: MRI tumor segmentation supports diagnosis, treatment planning, protection of healthy tissue, and longitudinal monitoring of recurrence or growth.Manual delineation is labor-intensive, slice-by-slice, subjective, and difficult to reproduce.
- Clinical motivation: Tumor heterogeneity in size, shape, location, and appearance makes automatic segmentation challenging.Low-grade glioma boundaries may be invisible or blurry because tumors are often non-enhancing.
- Prior approaches: Deep CNNs learn increasingly complex features directly from data rather than relying on hand-crafted features.CNN-based methods have ranked highly in BRATS benchmarking, but segmentation remains difficult, especially for LGG and sub-tumoral regions.
- Prior approaches: Existing CNN-based methods require considerable computing resources, motivating attention to computational efficiency.This limitation is stated as an ongoing challenge for brain tumor segmentation methods.
- Study objective: The proposed method uses a 2D U-Net-based fully convolutional network with comprehensive augmentation and Soft Dice loss for imbalanced tumor regions.It was evaluated on both LGG and HGG datasets and reported comparable complete-tumor and superior core-tumor segmentation against state-of-the-art methods.
2 Method
The method combines multimodal BRATS MRI data, targeted preprocessing, augmentation, and a U-Net architecture for automatic segmentation of multiple tumor regions. Evaluation uses separate five-fold cross-validation for HGG and LGG cases with DSC and sensitivity.
- Data: BRATS 2015 provides 220 HGG and 54 LGG cases with T1, T1c, T2, and FLAIR MRI sequences.Manual labels cover necrosis, edema, non-enhancing tumor, and enhancing tumor.
- Data: FLAIR is used for complete and non-edema tumor regions, while T1c is used to delineate enhancing tumor.The authors state that this design improves efficiency and reduces required clinical inputs when multimodal data are unavailable.
- Data augmentation: Augmentation combines flipping, rotation, shifting, zooming, shearing, brightness changes, and elastic distortion.Elastic distortion is intended to generate additional samples with varied but plausible tumor shapes.
- Network architecture: The U-Net contains five convolutional blocks in a down-sampling path and an up-sampling path with skip architecture for detailed segmentation.Max pooling reduces feature-map size from 240×240 to 15×15, while the network outputs foreground and background segmentation without fully connected layers.
- Evaluation: Five-fold cross-validation separately evaluates HGG and LGG on complete, core, and enhancing tumor regions using DSC and sensitivity.Complete tumor includes all four labels; core tumor excludes edema; enhancing tumor contains only label 4.
3 Results and Discussion
The U-Net-based method combines comprehensive augmentation with 2D processing and achieved strong, efficient segmentation on BRATS 2015, though LGG enhancing-tumor segmentation remained difficult.
- Method: The proposed network couples U-Net skip architecture with rigid, affine, brightness, and elastic data augmentation for fully automatic segmentation.The method also uses a Soft Dice loss designed to accommodate unbalanced tumor-region samples.
- Segmentation results: Cross-validation demonstrated superior complete- and core-tumor segmentation results on the BRATS 2015 challenge datasets.The comparison covered recently published deep-learning methods evaluated on BRATS datasets.
- Segmentation results: 0.81 DSC was achieved for enhancing-tumor segmentation in the HGG cohort.For LGG cases, enhancing-tumor segmentation using T1c images alone was not successful.
- Implementation: A 2D network was selected because GPU memory allowed full-slice processing, whereas a 3D system could process only a small volume patch.Each HGG cross-validation training session required approximately 18 hours on an NVIDIA Titan X GPU; LGG training took about one-quarter as long.
- Efficiency: 2 to 3 seconds per case was required for prediction after model fixing, regardless of whether the study was HGG or LGG.The reported prediction time was lower than previously reported times of approximately 30 seconds, 25 seconds to 3 minutes, and 8 minutes.
- Limitations: The authors identify cross-validation, empirically tuned parameters, and weaker LGG enhancing-tumor segmentation as limitations.They suggest independent testing and multimodal training with HGG datasets as future directions.
4 Conclusion
The paper presents a fully automatic U-Net-based brain-tumor segmentation method evaluated on BRATS 2015. It reports efficient, robust segmentation, with results comparable for complete tumors and superior for core tumors relative to other state-of-the-art methods.
- Conclusion: The method provides fully automatic brain-tumor detection and segmentation using U-Net-based deep convolutional networks.The authors evaluated it on BRATS 2015 datasets containing both HGG and LGG patients.
- Conclusion: The method achieved comparable complete-tumor and superior core-tumor results relative to other state-of-the-art methods.Validation used a five-fold cross-validation scheme against manually delineated ground truth.
- Implications: The framework can generate patient-specific brain-tumor segmentations without manual interference for clinical tasks such as diagnosis, treatment planning, and monitoring.The authors identify independent, multi-institutional, and longitudinal datasets as further application directions.