WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Abstract

Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

“…The existing EDD‐Net 55 achieved Sy values of 88% for ET, 89% for WT, and 87% for TC, along with high Sp values. Implemented WD‐UNeXt 56 demonstrated high performance across all metrics. The existing focal cross‐transformer method 57 achieved moderate to high performance.…”

“…This section discusses, the analysis of outcomes accomplished from various simulation experiment for BT segmentation and OSP. The existing methods such as ResUNet+, 53 radiomics based automatic framework (RBAF), 35 DCNN, 54 encoder–decoder method with depthwise atrous spatial pyramid pooling Network (EDD‐Net), 55 Weight loss function and Dropout U‐Net with convNeXt block (WD‐UNeXt), 56 focal cross transformer, 57 Attention‐based Multimodal Glioma Segmentation (AMMGS), 58 Segmentation based on Transformer and U2‐Net (STrans‐U2Net), 59 deep Residual U‐Net (dRes U‐Net), 60 and DenseTransformer (DenseTrans) 61 are compared with the introduced approach based on DSC, Sy, Sp and Hausdorff 95 in Table 6.…”