Perceptual clustering for automatic hotspot detection from Ki-67-stained neuroendocrine tumour images
Perceptual clustering for automatic hotspot detection from Ki-67-stained neuroendocrine tumour images
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DOI：
10.1111/jmi.12176
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发表时间：
2014-12-01
影响因子：
2
通讯作者：
Gurcan, Metin N.
中科院分区：
文献类型：
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作者：
Niazi, M. Khalid Khan;Yearsley, Martha M.;Gurcan, Metin N.
热点检测在消化系统神经内分泌肿瘤的分级中起着至关重要的作用。热点通常从Ki-67染色的图像中手动检测，这是一种繁琐、不可再现且容易出错的实践。我们报告了一种新的方法来分割Ki-67阳性细胞核的Ki-67染色切片的神经内分泌肿瘤。该方法结合最小图割沿着与多状态差异的高斯检测单个细胞从图像的Ki-67染色的载玻片。然后，它自动定义复合函数，该函数用于确定神经内分泌肿瘤载玻片图像中的热点。我们结合联合收割机改进的粒子群优化算法与消息传递聚类，以模仿病理学家在神经内分泌肿瘤载玻片图像热点检测过程中的思维过程。所提出的方法进行了测试，55幅图像的大小为10 × 5 K，并导致在94.60%的准确性。开发的方法也可以成为其他疾病（如乳腺癌和胶质母细胞瘤）工作流程的一部分。
Hotspot detection plays a crucial role in grading of neuroendocrine tumours of the digestive system. Hotspots are often detected manually from Ki-67-stained images, a practice which is tedious, irreproducible and error prone. We report a new method to segment Ki-67-positive nuclei from Ki-67-stained slides of neuroendocrine tumours. The method combines minimal graph cuts along with the multistate difference of Gaussians to detect the individual cells from images of Ki-67-stained slides. It, then, automatically defines the composite function, which is used to determine hotspots in neuroendocrine tumour slide images. We combine modified particle swarm optimization with message passing clustering to mimic the thought process of the pathologist during hotspot detection in neuroendocrine tumour slide images. The proposed method was tested on 55 images of size 10 x 5 K and resulted in an accuracy of 94.60%. The developed methodology can also be part of the workflow for other diseases such as breast cancer and glioblastomas.