Institute of Pathology, University Hospital Bonn, Bonn, Germany.
Institute of Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Bonn, Germany.
APMIS. 2022 Jan;130(1):26-33. doi: 10.1111/apm.13188. Epub 2021 Nov 25.
This study aimed to clarify whether the pattern recognition involved in scoring proliferation fractions can be trained by abstract computerized images of virtual tissues. Twenty computer-generated images with randomly distributed blue or red dots were scored by 12 probands (all co-workers or collaborators of the Institute of Pathology, University of Bonn). Afterward, the probands underwent a training phase during which they received an immediate feedback on the actual rate of positivity after each image. Finally, the initial testing series was rescored. In a second round with 15 different probands, 20 Ki-67 immunohistochemistry images of tonsil tissue were scored, followed by the same training phase with computer-generated images, before the immunohistochemistry slides were scored again. Paired t-tests were used to compare the differences in mean rates pre- and post-training. Concerning computerized images, untrained probands scored the percentages of positive dots with a mean deviation from the true rates of 8.2%. Following training, the same testing series was scored significantly better with a mean deviation of 4.9% (mean improvement 3.3%, p < 0.001). Scoring real immunohistochemistry slides, the training with computerized images also improved correct estimations, albeit to a lesser degree (mean improvement 1%, p = 0.03). Abstract computerized images of virtual tissues may be a useful tool to train and improve the accuracy of pattern recognition involved in semiquantitative scoring of immunohistochemistry slides. As a side results, this study highlights the value of computer-generated images to verify the performance of image-analysis software.
本研究旨在阐明通过虚拟组织的抽象计算机化图像是否可以训练参与评分增殖分数的模式识别。使用 12 名受试者（均为波恩大学病理学研究所的同事或合作者）对 20 张具有随机分布的蓝色或红色点的计算机生成图像进行评分。之后，受试者接受了一个培训阶段，在每个图像后他们都能立即获得实际阳性率的反馈。最后，对初始测试系列进行了重新评分。在第二轮中，有 15 名不同的受试者对 20 个扁桃体组织的 Ki-67 免疫组化图像进行了评分，然后对计算机生成的图像进行了相同的培训阶段，之后再次对免疫组化载玻片进行评分。采用配对 t 检验比较训练前后平均阳性率的差异。对于计算机化图像，未经训练的受试者对阳性点百分比的评分平均与真实率偏差 8.2%。经过培训，同一测试系列的评分明显更好，平均偏差为 4.9%（平均提高 3.3%，p<0.001）。对真实的免疫组化载玻片进行评分，使用计算机化图像进行培训也可以提高正确估计的程度，尽管程度较小（平均提高 1%，p=0.03）。虚拟组织的抽象计算机化图像可能是训练和提高参与免疫组化载玻片半定量评分的模式识别准确性的有用工具。作为一个次要结果，本研究强调了计算机生成图像对于验证图像分析软件性能的价值。
