Su Hai, Xing Fuyong, Kong Xiangfei, Xie Yuanpu, Zhang Shaoting, Yang Lin
J. Crayton Pruitt Family Dept. of Biomedical Engineering, University of Florida, FL 32611.
Department of Electrical and Computer Engineering, University of Florida, FL 32611.
Med Image Comput Comput Assist Interv. 2015 Oct;9351:383-390. doi: 10.1007/978-3-319-24574-4_46. Epub 2015 Nov 18.
Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.
计算机辅助诊断（CAD）是一种用于准确且一致的诊断和预后评估的有前景的工具。细胞检测和分割是CAD的关键步骤。由于细胞形状的变化、相互接触的细胞以及杂乱的背景，这些任务具有挑战性。在本文中，我们提出了一种使用具有平凡模板的稀疏重建和堆叠去噪自动编码器（sDAE）的细胞检测和分割算法。稀疏重建通过将测试补丁表示为学习字典中形状的线性组合来处理形状变化。平凡模板用于对相互接触的部分进行建模。使用原始数据及其结构化标签进行训练的sDAE用于细胞分割。据我们所知，这是第一项将稀疏重建和带有结构化标签的sDAE应用于细胞检测和分割的研究。所提出的方法在两个包含从脑肿瘤和肺癌图像中获取的3000多个细胞的数据集上进行了广泛测试。与其他现有技术相比，我们的算法取得了最佳性能。
