Comparative study on kidney tumor segmentation for partial nephrectomy planning using hybrid CNN-transformer networks

To improve the segmentation performance of kidney tumors in abdominal CT images, this study proposes two hybrid methods that combine convolutional neural networks (CNNs) with transformer-based network. Method A extracts features using a CNN block and then uses them as inputs to a transformer-based network. Method B employs a dual encoder structure with both CNN and transformer encoders. Experimental results showed that Method B significantly outperforms Method A in terms of balanced accuracy, Dice Similarity Coefficient (DSC). Method B demonstrates improved detection and segmentation of small tumors, reducing over-segmentation and substantially enhancing under-segmentation compared to Method A.