PyDiNet: Pyramid Dilated Network for medical image segmentation

Gridach, Mourad

doi:10.1016/j.neunet.2021.03.023

Cited by 44 publications

(13 citation statements)

References 27 publications

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“…A classic version of this hybridization was described also in [7], where a transformer was placed between encoder (after flattening) and the decoder (before reshaping). A similar approach is the development of pyramid dilated module that uses many dilated convolutions in parallel [8] or applying self-attention modules [9]. In addition to parallel approaches, recursive methods are also used, as shown in [10].…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

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Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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“…Liu et al [ 12 ] input two CT images of different scales into a residual blocks based on dual-path network, and the network encoder extracts the global and local features of the image from residual blocks and rich contextual information of pulmonary nodules. Aiming at the problem of the loss of spatial information caused by U-Net pooling operation, Gridach [ 13 ] proposed a pyramid expansion network, which integrates multiple dilated convolutions with different dilated rates to capture the tiny details of the image. Channel attention mechanism is added to skip connection of U-Net for lung parenchymal segmentation by Wang et al [ 14 ], and the last layer of the network used a hybrid dilated attention convolution layer.…”

Section: Introductionmentioning

confidence: 99%

APU-Net: An Attention Mechanism Parallel U-Net for Lung Tumor Segmentation

Tao

Qiu

2022

BioMed Research International

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Lung cancer is one of the malignant tumors with high morbidity and mortality, and lung nodules are the early stages of lung cancer. The symptoms of pulmonary nodules are not obvious in the clinic, and the optimal treatment time is missed due to the missed diagnosis in the clinic. A parallel U-Net network called APU-Net is proposed. Firstly, two parallel U-Net networks are used to extract the features of different modalities. Among them, the subnetwork UNet_B extracts the CT image features, and the subnetwork UNet_A consists of two encoders to extract the PET/CT and PET image features. Secondly, multimodal feature extraction blocks are used to extract features for PET/CT and PET images in UNet_B network. Thirdly, a hybrid attention mechanism is added to the encoding paths of the UNet_A and UNet_B. Finally, a multiscale feature aggregation block is used for extracting feature maps of different scales of decoding path. On the lung tumor 18FDGPET/CT multimodal medical images dataset, experiments’ results show that the DSC, Recall, VOE, and RVD coefficients of APU-Net are 96.86%, 97.53%, 3.18%, and 3.29%, respectively. APU-Net can improve the segmentation accuracy of the adhesion between the lesion of complex shape and the normal tissue. This has positive significance for computer-aided diagnosis.

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“…A basic task in medical IS [1,2] is to extract specific organs and tumors from different types of medical images. Organ and tumor segmentation provides an important basis for cancer diagnosis, surgical planning, and pathological analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

Yang

2021

Scientific Programming

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Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

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PyDiNet: Pyramid Dilated Network for medical image segmentation

Cited by 44 publications

References 27 publications

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

APU-Net: An Attention Mechanism Parallel U-Net for Lung Tumor Segmentation

A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

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