Brain tumor segmentation of multi-modality MR images via triple intersecting U-Nets

Zhang, Jinjing; Zeng, Jianchao; Qin, Peng; Zhao, Lijun

doi:10.1016/j.neucom.2020.09.016

Cited by 54 publications

(18 citation statements)

References 38 publications

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“…The focal loss function in Equation ( 4 ) is applied to resolve class imbalance problems. The focal loss is provided as weights to pixels, in which k signifies the number of classes, which indicates that the pixels belong to the k th class, and P k is the predicted probability, and p indicates a high probability that is easier to classify accurately [ 62 ]. The focal loss function value is 10, and weights are allocated based on the complexity that classifies the pixels effectively.…”

Section: Experiments Results and Comparative Analysismentioning

confidence: 99%

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Gull

Akbar

Khan

2021

BioMed Research International

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Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient’s life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the MR images are filtered to eliminate the noise and are improve the contrast. For segmentation of brain tumor images, the proposed CNN architecture is used, and for postprocessing, the global threshold technique is utilized to eliminate small nontumor regions that enhanced segmentation results. In classification, GoogleNet model is employed on three publicly available datasets. The experimental results depict that the proposed method is achieved average accuracies of 96.50%, 97.50%, and 98% for segmentation and 96.49%, 97.31%, and 98.79% for classification of brain tumor on BRATS2018, BRATS2019, and BRATS2020 datasets, respectively. The outcomes demonstrate that the proposed framework is effective and efficient that attained high performance on BRATS2020 dataset than the other two datasets. According to the experimentation results, the proposed framework outperforms other recent studies in the literature. In addition, this research will uphold doctors and clinicians for automatic diagnosis of brain tumor disease.

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Section: Experiments Results and Comparative Analysismentioning

confidence: 99%

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Gull

Akbar

Khan

2021

BioMed Research International

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“…But the algorithm is more complicated and time-consuming. Zhang et al [24] proposed a triple crossover U-Nets (TIU-Nets) for glioma segmentation. The proposed TIU-Nets are composed of binary class segmentation U-Net (BU-Net) and multiclass segmentation U-Net (MU-Net) and have achieved better performance.…”

Section: Introductionmentioning

confidence: 99%

Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module

Tao

Wang

2022

Computational and Mathematical Methods in Medicine

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Traditional image segmentation methods often encounter problems of low segmentation accuracy and being time-consuming when processing complex tooth Computed Tomography (CT) images. This paper proposes an improved segmentation method for tooth CT images. Firstly, the U-Net network is used to construct a tooth image segmentation model. A large number of feature maps in downsampling are supplemented to downsampling to reduce information loss. At the same time, the problem of inaccurate image segmentation and positioning is solved. Then, the attention module is introduced into the U-Net network to increase the weight of important information and improve the accuracy of network segmentation. Among them, subregion average pooling is used instead of global average pooling to obtain spatial features. Finally, the U-Net network combined with the improved attention module is used to realize the segmentation of tooth CT images. And based on the image collection provided by West China Hospital for experimental demonstration, compared with other algorithms, our method has better segmentation performance and efficiency. The contours of the teeth obtained are clearer, which is helpful to assist the doctor in the diagnosis.

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“…Contour detection, a fundamental concept in image analysis and understanding, has been employed in several areas, such as medical image segmentation, object recognition, and scene understanding. With the exponential growth and success of AI, an increasing number of deep learning-based contour detection systems are being used in medical image research [1,2,3,4], and diagnostic studies [4]. Our focus in this work is to develop a robust contour detection model of retinal layers from an OCT study.…”

Section: Introductionmentioning

confidence: 99%

Cu-Net: Towards Continuous Multi-Class Contour Detection for Retinal Layer Segmentation In Oct Images

Bhattarai

Kambhamettu

Jin³

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Recent deep learning-based contour detection studies show high accuracy in single-class boundary detection problems. However, this performance does not translate well in a multi-class scenario where continuous contours are required. Our research presents CU-Net, a U-Net-based network with residual-net encoders which can produce accurate and uninterrupted contour lines for multiple classes. The critical factor behind this concept is our continuity module, containing an interpolation layer and a novel activation function that converts discrete signals into smooth contours. We find the application of our approach in medical imaging problems like retinal layer segmentation from optical coherence tomography (OCT) scans. We applied our method to an expert annotated OCT dataset of children with sickle-cell disease. To compare with benchmarks, we evaluated our network on DME and HC-MS datasets. We achieved an overall mean absolute distance of 6.48 ± 2.04µM and 1.97 ± 0.89µM , respectively 1.03 and 1.4 times less than the current state-of-the-art.

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Brain tumor segmentation of multi-modality MR images via triple intersecting U-Nets

Cited by 54 publications

References 38 publications

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module

Cu-Net: Towards Continuous Multi-Class Contour Detection for Retinal Layer Segmentation In Oct Images

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