Abstract
The segmentation of brain tumors in medical images is a crucial step of clinical treatment. Manual segmentation is time consuming and labor intensive, and existing automatic segmentation methods suffer from issues such as numerous parameters and low precision. To resolve these issues, this study proposes a learnable group convolution-based segmentation method that replaces convolution in the feature extraction stage with learnable group convolution, thereby reducing the number of convolutional network parameters and enhancing communication between convolution groups. To improve utilization of the feature maps, we added a skip connection structure between learnable group convolution modules, which increased segmentation precision. We used deep supervision to combine output images in the network output stage to reduce overfitting and enhance the recognition capabilities of the network. We tested the proposed algorithm model using the open BraTS 2018 dataset. The experiment results revealed that the proposed model is superior to 3D U-Net and DMFNet and has better segmentation results for tumor cores than No New-Net and NVDLMED, the winning methods in the BraTS 2018 challenge. The segmentation precision of the proposed method with regard to whole tumors, enhancing tumors, and tumor cores was 90.25%, 80.36%, and 86.20%. Furthermore, the proposed method uses fewer parameters and a less complex model.
Highlights
Diagnosis is crucial for the surgical treatment of brain tumors. is has been aided by recent advances in medical imaging technology
Segmentation accuracy was gauged using the Dice similarity coefficient and the Hausdorff distance. e former indicates the degree of similarity between the experimental segmentation results and the ground truth, with a higher value indicating greater segmentation precision. e latter calculates the maximum distance between the contours of the segmentation results and the ground truths to indicate the segmentation quality of the tumor boundaries, and a smaller absolute value represents better segmentation performance
Brain tumors vary significantly in intensity and are irregular in shape. is study modified dilated multifiber network (DMFNet) to use fewer parameters and introduced learnable group convolution (LGConv) to the feature extraction stage so that it can flexibly choose the number of groups based on dataset and network characteristics. is facilitates adaptation to more complex data features and has wider applicability
Summary
Diagnosis is crucial for the surgical treatment of brain tumors. is has been aided by recent advances in medical imaging technology. Is has been aided by recent advances in medical imaging technology. Magnetic resonance imaging (MRI) technology can display brain tissue information in great detail and is widely used for the diagnosis of brain tumors. Four types of MRI modes are used: T1 weighted, T2 weighted, postcontrast T1 weighted, and fluid-attenuated inversion recovery (FLAIR). Each of these reflects different aspects of brain tissue. E accurate segmentation of brain tumors in medical images is a critical step before treatment. Manual segmentation is time consuming and labor intensive, and as a result, efficient and accurate automatic segmentation methods have become a popular research topic in recent years. Brain tumor segmentation methods can generally be divided into three categories: manual segmentation, semiautomatic segmentation, and fully-automatic segmentation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
