DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation

Zhang, Hanxiao; Li, Jingxiong; Shen, Mali; Wang, Yaqi; Yang, Guang‐Zhong

doi:10.1007/978-3-030-46643-5_20

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…We compared our results with the other six state‐of‐the‐art approaches 29‐34 . Cheng, et al 29 used spatial‐channel relation learning for brain tumor segmentation.…”

Section: Methodsmentioning

confidence: 99%

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A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

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Purpose To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimension radiomic features extracted from four sequences of MRIs and 128‐dimension high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including nonenhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross validation, the model was independently validated using the rest 35 patients from the same database. Results The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

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“…We compared our results with the other six state‐of‐the‐art approaches 29‐34 . Cheng, et al 29 used spatial‐channel relation learning for brain tumor segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…Cheng, et al 31 designed effective and efficient multitask learning for brain tumor segmentation. Zhang, et al 32 developed a DDU‐Nets for glioma segmentation. Kotowski, et al 33 proposed a cascaded U‐Net architecture to perform detection and segmentation of brain tumors from MR scans.…”

Section: Methodsmentioning

confidence: 99%

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

View full text Add to dashboard Cite

show abstract

“…We compared our results with the other six state‐of‐the‐art approaches 29‐34 . Cheng et al 29 use Spatial‐channel relation learning for brain tumor segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…We compared our results with the other six state-ofthe-art approaches. [29][30][31][32][33][34] Cheng et al 29 use Spatialchannel relation learning for brain tumor segmentation. Sun et al 30 propose a novel model based on 3D fully convolutional network to create an automated and accurate segmentation.…”

Section: Experiments Settingmentioning

confidence: 99%

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

View full text Add to dashboard Cite

Purpose To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimensions radiomics features extracted from four sequences of MRIs and 128‐dimensions high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non‐enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross‐validation, and the model was independently validated using the rest 35 patients from the same database. Results The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

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“…Although the training sample was expanded with synthetic data, this approach did not yield more prominent results. DDU-Nets ( 45 ) contain three models of distributed dense connectivity. DDU-Nets are not particularly effective overall, although they slightly outperform our method in terms of Hausdorff distance scores on WT and ET.…”

Section: Methodsmentioning

confidence: 99%

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Wang

Luan

et al. 2022

Front. Oncol.

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Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.

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DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation

Cited by 14 publications

References 11 publications

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

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