Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net

Khorasani, Amir; Kafieh, Rahele; Saboori, Masih; Tavakoli, Mehdi

doi:10.1007/s13246-022-01164-w

Cited by 11 publications

(2 citation statements)

References 66 publications

(67 reference statements)

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“…So, segmentation has received important attention in recent years and various systems have been proposed, but an accurate one is still a challenge. 8 It is caused by various reasons, such as the appearance of glioma anywhere with different sizes, appearances, and shapes. In addition, this tumor has blurred boundaries with healthy tissue.…”

Section: Introductionmentioning

confidence: 99%

A deep conventional neural network model for glioma tumor segmentation

Ayadi

Elhamzi

Atri

2023

Int J Imaging Syst Tech

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Glioma represents one of the most aggressive cancers, which can develop in the brain. The automatic tumor segmentation and its sub‐regions represent a challenging task owing to their considerable structural variation. It can appear in different ways and with several shapes, which makes tissue identification a crucial task. Reliable and accurate segmentation presents an important component in tumor treatment and diagnosis planning. To overcome these drawbacks, various Deep Learning (DL) schemes are proposed to aid doctors. In this paper, a novel Convolutional Neural Network (CNN) scheme for glioma segmentation was proposed. Our suggested technique consists of three phases. First, we use intensity normalization to ameliorate the image quality as a preprocessing step. Second, an automatic segmentation technique based on CNN has been proposed. The new model has several layers. Finally, and with the goal of refining the segmentation results, we employ a post‐processing approach. We use the public benchmark BraTS datasets from 2018 and 2020 with low‐grade and high‐grade glioma tumors to test the suggested framework. These datasets contain about 285 and 369 patients, respectively. The four modalities are exploited. Each patient has about 155 2D images from every modality. All the images have the same size (240 × 240 pixels). Our technique performs well compared to new methods, with Dice scores of 0.88 for the Whole Tumor, 0.84 for Tumor Core, and 0.71 for Enhancing Tumor based on the first dataset. According to the second dataset, the three regions had an average of 0.88, 0.9, and 0.75, respectively. The Jaccard indexes for the first data set are 0.8, 0.73, and 0.56 for the three regions, respectively. The second data set attains 0.8, 0.82, and 0.6 for the three regions. The results show that the suggested framework is an excellent way to segment data, especially compared to other methods.

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Section: Introductionmentioning

confidence: 99%

A deep conventional neural network model for glioma tumor segmentation

Ayadi

Elhamzi

Atri

2023

Int J Imaging Syst Tech

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“…Ronneberger et al (2016) proposed the U-Net model based on the improved FCN [17]. The model achieves good segmentation performance through a small number of labeled images and is widely applied in biomedical fields [18,19] such as retinal vessel segmentation [20] and cell boundary segmentation [21]. Zhou et al (2018) [22] redesigned the jump path based on U-Net for reducing the semantic gap between the feature mapping of encoder and decoder.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Based on DAF-Net++ Model for Wood Annual Rings Segmentation

Zhuang

Zhang²,

Li-ming

et al. 2023

Electronics

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The semantic segmentation of annual rings is a research topic of interest in wood chronology. To solve the problem of wood annual rings being difficult to segment in dense areas and being greatly affected by defects such as cracks and wormholes, this paper builds a DAF-Net++ model which is based on U-Net whose backbone network is VGG16 and filled with dense jump links, CBAM and DCAM. In this model, VGG16 is used to enhance the extraction ability of image features, dense jump links are used to fuse semantic information of different levels, DCAM provides weighting guidance for shallow features, and CBAM solves the loss of down-sampling information. Taking a Chinese fir wood as the experimental object, 1700 CT images of wood transverse section were obtained by medical CT equipment and 120 of them were randomly selected as the dataset, which was expanded by cropping and rotation, among others. DAF-Net++ was used for training the model and segmentation of the annual rings, and finally the performance of the model was evaluated. The training method is freeze training followed by thaw training, and takes Focal Loss as the loss function, ReLU as the activation function, and Adam as the optimizer. The experimental results show that, in the segmentation of CT images of Chinese fir annual rings, the MIoU of DAF-Net++ is 93.67%, the MPA is 96.76%, the PA is 96.63%, and the Recall is 96.76%. Compared with other semantic segmentation models such as U-Net, U-Net++, DeepLabv3+, etc., DAF-Net++ has better segmentation performance.

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