Context Aware 3D UNet for Brain Tumor Segmentation

Ahmad, Parvez; Qamar, Saqib; Shen, Linlin; Saeed, Adnan

doi:10.1007/978-3-030-72084-1_19

Cited by 28 publications

(25 citation statements)

References 13 publications

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“…The results presented here are in the same range of reported findings of studies that used additional contextual information during model training. Overall, the reported results in literature and the ones obtained in this study show that the inclusion of context-awareness, by means of model architecture changes or additional information as input to the network, has marginal or no improvement on glioma segmentation [ 15 , 16 , 17 , 18 , 19 , 20 , 22 , 23 , 24 ]. This should not discourage future research on the topic, but instead promote studies that exploit contextual information for brain tumor segmentation by other approaches and perhaps a combination of the currently implemented methods, i.e., context-aware blocks and additional contextual information as input to the network.…”

Section: Discussionsupporting

confidence: 58%

“…A number of attempts have been made to evaluate the impact of the introduction of context-aware blocks in the model architecture on brain tumor segmentation [ 16 , 17 , 18 , 19 , 20 ]. For example, Pei et al [ 19 ] used a context-aware deep neural network which thanks to a context encoding module between the encoder and the decoder part of the network, helped in overcoming the class imbalance problem that challenges brain tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?

2021

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Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning. Recently, convolutional neural networks have shown remarkable performance in the identification of tumor regions in magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy by using context-aware mechanisms. In this work, it is investigated whether or not the addition of contextual information from the brain anatomy in the form of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset was used to train and test two standard 3D U-Net (nnU-Net) models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP). For comparison, a baseline model (BLM) that only used the conventional MR image modalities was also trained. The impact of adding contextual information was investigated in terms of overall segmentation accuracy, model training time, domain generalization, and compensation for fewer MR modalities available for each subject. Median (mean) Dice scores of 90.2 (81.9), 90.2 (81.9), and 90.0 (82.1) were obtained on the official BraTS2020 validation dataset (125 subjects) for BLM, CIM, and CIP, respectively. Results show that there is no statistically significant difference when comparing Dice scores between the baseline model and the contextual information models (p > 0.05), even when comparing performances for high and low grade tumors independently. In a few low grade cases where improvement was seen, the number of false positives was reduced. Moreover, no improvements were found when considering model training time or domain generalization. Only in the case of compensation for fewer MR modalities available for each subject did the addition of anatomical contextual information significantly improve (p < 0.05) the segmentation of the whole tumor. In conclusion, there is no overall significant improvement in segmentation performance when using anatomical contextual information in the form of either binary WM, GM, and CSF masks or probability maps as extra channels.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?

2021

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“…The model achieved dice scores of 88.58%, 82.97%, and 79% for the whole tumor, core tumor, and enhanced tumor. Another modified architecture of U-Net was proposed by Parvez Ahmad et al [35] for automatic brain tumor segmentation. The author extracts multi-contextual features by using dense connections between encoder and decoder.…”

Section: Using Dropout Regularizationmentioning

confidence: 99%

RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images

et al. 2021

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The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.

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“…It contains a contracting path for extracting features and a symmetric expanding path for up-sampling to form a U-shaped architecture. Based on Unet, researchers developed a large collection of variants [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] to further improve segmentation performance. For example, Milletari et al [15] presented V-net, which is the 3D version of Unet, to process 3D data.…”

Section: Related Workmentioning

confidence: 99%