Zhou Yin, Chang Hang, Barner Kenneth E, Parvin Bahram
Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A; University of Delaware, Newark, DE, U.S.A.
Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A; Department of Electrical and Computer Engineering, University of California, Riverside, U.S.A.
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:1284-1287. doi: 10.1109/ISBI.2015.7164109. Epub 2015 Jul 23.
Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g., cell fate, cell cycle), which are compounded by the batch effect (e.g., variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation. Specifically, given raw image patches and the corresponding annotated binary masks, our algorithm jointly learns a bank of convolutional filters and a sparse linear regressor, where the former is used for feature extraction, and the latter aims to produce a likelihood for each pixel being nuclear region or background. During classification, the pixel label is simply determined by a thresholding operation applied on the likelihood map. The method has been evaluated using the benchmark dataset collected from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our method outperforms traditional nuclei segmentation algorithms and is able to achieve competitive performance compared to the state-of-the-art algorithm built upon human-designed features with biological prior knowledge.
在组织学切片中对细胞核结构进行自动分析，有可能有助于预测临床结果。然而，由于细胞核多形性和细胞状态（如细胞命运、细胞周期），这项任务具有挑战性，而批效应（如固定和染色的变化）又使情况变得更加复杂。目前用于细胞核分割的方法是基于人为设计的特征，可能无法有效地捕捉细胞核的内在结构。在本文中，我们提出了一种名为稀疏约束卷积回归（SCCR）的新方法用于细胞核分割。具体来说，给定原始图像块和相应的带注释的二值掩码，我们的算法联合学习一组卷积滤波器和一个稀疏线性回归器，前者用于特征提取，后者旨在为每个像素属于核区域或背景生成一个似然值。在分类过程中，像素标签简单地由应用于似然图的阈值操作确定。该方法已使用从癌症基因组图谱（TCGA）收集的基准数据集进行了评估。实验结果表明，我们的方法优于传统的细胞核分割算法，并且与基于具有生物学先验知识的人为设计特征构建的最新算法相比，能够实现具有竞争力的性能。
