肾肿瘤是泌尿外科常见疾病之一,这些肿瘤的精确分割在帮助医生提高诊断准确性和治疗有效性方面起着至关重要的作用。然而,与肾肿瘤相关的固有挑战,比如模糊的边界,形态学变异,以及大小和位置的不确定性,肾肿瘤的准确分割仍然是医学图像分割领域的重大挑战。随着深度学习的发展,在医学图像分割领域取得了实质性的成果。然而,现有的模型在跨不同网络层次结构提取肾肿瘤特征方面缺乏特异性,这导致肾肿瘤特征的提取不足,进而影响肾肿瘤分割的准确性。为了解决这个问题,我们提出了选择性内核,视觉变压器,和协调注意力增强U-Net(STC-UNet)。该模型旨在增强特征提取,适应不同网络水平的肾肿瘤的独特特征。具体来说,在U-Net的浅层中引入了选择性内核模块,细节特征更丰富。通过选择性地使用不同尺度的卷积核,该模型增强了其在多个尺度上提取肾肿瘤详细特征的能力。随后,在网络的深层,特征地图较小但包含丰富的语义信息,VisionTransformer模块以非补丁方式集成。这些有助于模型全局捕获远程上下文信息。它们的非补丁实现有助于捕获细粒度特征,从而实现全局-局部信息的协同增强,最终加强模型对肾肿瘤语义特征的提取。最后,在解码器段中,提出了嵌入位置信息的协调注意力模块,旨在增强模型的特征恢复和肿瘤区域定位能力。我们的模型在KiTS19数据集上进行了验证,实验结果表明,与基线模型相比,STC-UNet显示改进1.60%,2.02%,2.27%,1.18%,1.52%,和1.35%的IoU,骰子,准确性,Precision,回想一下,和F1得分,分别。此外,实验结果表明,所提出的STC-UNet方法在视觉有效性和客观评价指标上都优于其他高级算法。
Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve diagnostic accuracy and treatment effectiveness. Nevertheless, inherent challenges associated with renal tumors, such as indistinct boundaries, morphological variations, and uncertainties in size and location, segmenting renal tumors accurately remains a significant challenge in the field of medical image segmentation. With the development of deep learning, substantial achievements have been made in the domain of medical image segmentation. However, existing models lack specificity in extracting features of renal tumors across different network hierarchies, which results in insufficient extraction of renal tumor features and subsequently affects the accuracy of renal tumor segmentation. To address this issue, we propose the Selective Kernel, Vision Transformer, and Coordinate Attention Enhanced U-Net (STC-UNet). This model aims to enhance feature extraction, adapting to the distinctive characteristics of renal tumors across various network levels. Specifically, the Selective Kernel modules are introduced in the shallow layers of the U-Net, where detailed features are more abundant. By selectively employing convolutional kernels of different scales, the model enhances its capability to extract detailed features of renal tumors across multiple scales. Subsequently, in the deeper layers of the network, where feature maps are smaller yet contain rich semantic information, the Vision Transformer modules are integrated in a non-patch manner. These assist the model in capturing long-range contextual information globally. Their non-patch implementation facilitates the capture of fine-grained features, thereby achieving collaborative enhancement of global-local information and ultimately strengthening the model\'s extraction of semantic features of renal tumors. Finally, in the decoder segment, the Coordinate Attention modules embedding positional information are proposed aiming to enhance the model\'s feature recovery and tumor region localization capabilities. Our model is validated on the KiTS19 dataset, and experimental results indicate that compared to the baseline model, STC-UNet shows improvements of 1.60%, 2.02%, 2.27%, 1.18%, 1.52%, and 1.35% in IoU, Dice, Accuracy, Precision, Recall, and F1-score, respectively. Furthermore, the experimental results demonstrate that the proposed STC-UNet method surpasses other advanced algorithms in both visual effectiveness and objective evaluation metrics.