Cheng Ching-Hsue, Yang Jun-He, Hsu Yu-Chen
Department of Information Management, National Yunlin University of Science & Technology, Yunlin.
Department of E-Sport Technology Management, Cheng Shiu University, Kaohsiung City.
Quant Imaging Med Surg. 2025 Jan 2;15(1):455-472. doi: 10.21037/qims-24-1429. Epub 2024 Dec 20.
Image segmentation is crucial in medical diagnosis, helping to identify diseased areas in images for more accurate diagnoses. The U-Net model, a convolutional neural network (CNN) widely used for medical image segmentation, has limitations in extracting global features and handling multi-scale pathological information. This study aims to address these challenges by proposing a novel model that enhances segmentation performance while reducing computational demands.
We introduce the LUNeXt model, which integrates Vision Transformers (ViT) with a redesigned convolution block structure. This model employs depthwise separable convolutions to capture global features with fewer parameters. Comprehensive experiments were conducted on four diverse medical image datasets to evaluate the model's performance.
The LUNeXt model demonstrated competitive segmentation performance with a significant reduction in parameters and floating-point operations (FLOPs) compared to traditional U-Net models. The application of explainable AI techniques provided clear visualization of segmentation results, highlighting the model's efficacy in efficient medical image segmentation.
LUNeXt facilitates efficient medical image segmentation on standard hardware, reducing the learning curve and making advanced techniques more accessible to practitioners. This model balances the complexity and parameter count, offering a promising solution for enhancing the accuracy of pathological feature extraction in medical images.
图像分割在医学诊断中至关重要，有助于识别图像中的病变区域以实现更准确的诊断。U-Net模型是一种广泛用于医学图像分割的卷积神经网络（CNN），在提取全局特征和处理多尺度病理信息方面存在局限性。本研究旨在通过提出一种新颖的模型来应对这些挑战，该模型在提高分割性能的同时降低计算需求。
我们引入了LUNeXt模型，它将视觉Transformer（ViT）与重新设计的卷积块结构相结合。该模型采用深度可分离卷积以用更少的参数捕获全局特征。在四个不同的医学图像数据集上进行了全面实验，以评估该模型的性能。
与传统U-Net模型相比，LUNeXt模型展现出具有竞争力的分割性能，参数和浮点运算（FLOP）显著减少。可解释人工智能技术的应用为分割结果提供了清晰的可视化，突出了该模型在高效医学图像分割中的功效。
LUNeXt有助于在标准硬件上进行高效的医学图像分割，降低学习曲线并使从业者更容易获得先进技术。该模型平衡了复杂性和参数数量，为提高医学图像中病理特征提取的准确性提供了一个有前景的解决方案。
