基于肾脏医学图像的诊断分割

Shixuemei, Mideth Abisado
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引用次数: 0

摘要

医学图像的病灶分割是智能医学的重要组成部分。随着深度学习技术的发展,医学图像的病灶分割技术也得到了快速发展。目前的分割技术虽然可以保留空间特征,但保留的空间特征不足,分割精度不高。我们提出的PST-UNet模型将变压器与u型结构相结合,通过卷积融合模块更好地融入编码器的多尺度特征。PST-UNet模型分别在编码器端和解码器端采用两种块Swin变换。肾脏病变数据呈正态分布。因此,为了保留更多的空间特征,提高肾脏病变分割的精度,在编码器端引入了Swin变压器块和全GELU(高斯误差线性单元)激活函数。同样,在解码器端,还引入了Swin变压器块、全GELU激活函数、卷积融合模块的上采样和跳线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnostic Segmentation Based on Kidney Medical Image
Lesion segmentation of medical images is an important component of smart medicine. The development of deep learning technology is followed by rapid advancement in lesion segmentation technology of medical images. Though the present segmentation technology can retain spatial features, insufficient spatial features are retained with low segmentation accuracy. Our proposed PST-UNet model combines transformer with U-shaped structure and better infuses encoder's multi-scale features by using convolution fusion module. PST-UNet model adopts two types of block Swin transform at encoder and decoder ends respectively. Renal lesion data tends to present a normal distribution. Therefore, to preserve more spatial features and enhance the precision of renal lesion segmentation, Swin transformer block and full GELU (Gaussian Error Linear Unit) activation function are introduced at the encoder end. Similarly, at the decoder end, Swin transformer block, full GELU activation function, up-sampling and jumper wires from the convolution fusion module are also introduced.
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