对比手工与机器提取特征，提升前列腺癌图像分割精度
Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation
- 用纹理描述符结合手工特征，提取像素级空间关系
- 机器学习方法在分类准确率达94%，优于手工特征
- 深度网络更适合临床级像素级分割任务
组织病理学图像分析是前列腺癌识别的可靠方法。本文对比了两种用于自动分割前列腺图像腺体结构以实现格里森分级的方法。第一种采用手工设计的学习技术，结合灰度共生矩阵（GLCM）和局部二值模式（LBP）纹理描述符，突出像素级的空间依赖性并减少信息损失。第二种使用U-Net卷积神经网络进行语义分割，提取机器驱动特征。基于支持向量机的手工特征分类准确率分别达到99.0%（GLCM）和95.1%（LBP），而基于U-Net的机器特征达到94%。此外，通过杰卡德和骰子系数评估，U-Net在组织学分级1至4中均表现出更优的分割质量。本研究凸显了机器驱动特征在依赖像素级分割的临床应用中的价值。
原文摘要 · Abstract (English)
Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two approaches for segmenting glandular structures in prostate images to automate Gleason grading. The first approach utilizes a hand-crafted learning technique, combining Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) texture descriptors to highlight spatial dependencies and minimize information loss at the pixel level. For machine driven feature extraction, we employ a U-Net convolutional neural network to perform semantic segmentation of prostate gland stroma tissue. Support vector machine-based learning of hand-crafted features achieves impressive classification accuracies of 99.0% and 95.1% for GLCM and LBP, respectively, while the U-Net-based machine-driven features attain 94% accuracy. Furthermore, a comparative analysis demonstrates superior segmentation quality for histopathological grades 1, 2, 3, and 4 using the U-Net approach, as assessed by Jaccard and Dice metrics. This work underscores the utility of machine-driven features in clinical applications that rely on automated pixel-level segmentation in prostate tissue images.
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