MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data

Sultan, Haseeb; Owais, Muhammad; Nam, Se Hyun; Haider, Adnan; Akram, Rehan; Usman, Muhammad; Park, Kang Ryoung

doi:10.1016/j.jksuci.2023.101560

Cited by 7 publications

(11 citation statements)

References 32 publications

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“…Their method required pre-processing for effective results. Our proposed PFA-Net achieved superior quantitative results and required fewer network parameters than the method proposed by [19], without the need for any pre-processing steps.…”

Section: Complete Heterogeneous Dataset-based Methodsmentioning

confidence: 95%

“…A DL-based model, MDFU-Net, was developed for heterogeneous brain dataset analysis [19], yielding quantitative results with a DS of 62.66%. Their method required pre-processing for effective results.…”

Section: Complete Heterogeneous Dataset-based Methodsmentioning

confidence: 99%

“…Several CAD frameworks have been developed for BT classification and segmentation based on hand-and DL-extracted features methods [16][17][18][19][20][21]. We propose a CAD tool utilizing DL-extracted features for the robust segmentation of three different BT regions of the multimodal brain tumor segmentation (BraTS)-2020 dataset [9,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network

Sultan,

Ullah,

Hong

et al. 2024

Fractal Fract

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The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased because of the diffuse and irregular nature of BTs, along with varying enhancement patterns and the coexistence of different tumor components. Hence, the development of an automated diagnostic system for BTs is vital for mitigating subjective bias and achieving speedy and effective BT segmentation. Recently developed deep learning (DL)-based methods have replaced subjective methods; however, these DL-based methods still have a low performance, showing room for improvement, and are limited to heterogeneous dataset analysis. Herein, we propose a DL-based parallel features aggregation network (PFA-Net) for the robust segmentation of three different regions in a BT scan, and we perform a heterogeneous dataset analysis to validate its generality. The parallel features aggregation (PFA) module exploits the local radiomic contextual spatial features of BTs at low, intermediate, and high levels for different types of tumors and aggregates them in a parallel fashion. To enhance the diagnostic capabilities of the proposed segmentation framework, we introduced the fractal dimension estimation into our system, seamlessly combined as an end-to-end task to gain insights into the complexity and irregularity of structures, thereby characterizing the intricate morphology of BTs. The proposed PFA-Net achieves the Dice scores (DSs) of 87.54%, 93.42%, and 91.02%, for the enhancing tumor region, whole tumor region, and tumor core region, respectively, with the multimodal brain tumor segmentation (BraTS)-2020 open database, surpassing the performance of existing state-of-the-art methods. Additionally, PFA-Net is validated with another open database of brain tumor progression and achieves a DS of 64.58% for heterogeneous dataset analysis, surpassing the performance of existing state-of-the-art methods.

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Section: Complete Heterogeneous Dataset-based Methodsmentioning

confidence: 95%

Section: Complete Heterogeneous Dataset-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network

Sultan,

Ullah,

Hong

et al. 2024

Fractal Fract

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“…They also mentioned that an explainable artificial intelligence technique could be analyzed to discover complex prediction and decision-making strategies. The study [ 30 ] addressed two issues: (i) clinical brain tumor segmentation from homogenous data with high efficiency and (ii) heterogonous data analysis by constructing a multiscale dilated feature upsampling network (MDFU-Net). Incorporating multiscale detailed features (MDF) in the encoder module significantly enhanced segmentation performance.…”

Section: Literature Reviewmentioning

confidence: 99%

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

Md. Ashafuddula,

Islam

2024

International Journal of Biomedical Imaging

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Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the “ImageNet” collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model’s generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model’s outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F1-score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.

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“…Nowadays, medical image segmentation algorithms based on convolutional neural networks (CNNs) have become mainstream [ 7 , 8 , 9 , 10 ]. These algorithms include fully convolutional neural networks (FCNs), U-Net, and U-shaped network structures [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Distance Matters: A Distance-Aware Medical Image Segmentation Algorithm

Feng,

Cong,

Xing

et al. 2023

Entropy

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The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images.

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MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data

Cited by 7 publications

References 32 publications

Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network

Estimation of Fractal Dimension and Segmentation of Brain Tumor with Parallel Features Aggregation Network

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

Distance Matters: A Distance-Aware Medical Image Segmentation Algorithm

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