外周神经母细胞性肿瘤病理切片MKI的计算机辅助预后评估

Computer-Aided Prognosis Evaluation for MKI of Pathological Slices of Peripheral Neuroblastic Tumors

外周神经母细胞性肿瘤(peripheral neuroblastic tumors,pNT)是儿童常见的颅外恶性实体瘤,其主要预后评估依据为神经母细胞瘤分化程度和核碎裂指数(mitosiskaryorrhexis index,MKI)。目前,对MKI的计算主要通过病理医生人工计数,过程繁琐且工作量较大。采用计算机图像处理算法识别病理切片图像中病理性核分裂神经母细胞(pathological mitotic neuroblasts,PMN)和神经母细胞(neuroblasts,NEU),并辅助病理医生计数,可减少医生的重复性工作,提高工作效率。采用数学形态局部最小值标记(Hminima)修改梯度幅值,并利用改进型分水岭算法识别NEU并计数。实验结果表明,与病理医生的金标准对比,所提算法对NEU识别的平均准确率为94.2%,平均过分割率为2.79%。从色度分量角度对PMN的细胞质区域识别,平均识别准确率为81.66%,MKI值的平均误差率为0.031%。

Peripheral neuroblastic tumors(pNT)are common extracranial malignant solid tumors in children,and its main prognostic evaluation is based on differentiation degree of neuroblastic tumor and mitosiskaryorrhexis index(MKI).At present,the calculation of MKI is mainly done manually by pathologists,which is a cumbersome process with a large workload.The computer image processing algorithm is used to identify pathological mitotic neuroblasts(PMN)and neuroblasts(NEU)in pathological slice images,and assist pathologists in counting,which can reduce doctors’repetitive work and improve doctors’work effectiveness.The mathematical morphology local minimum mark(Hminima)is used to modify the gradient amplitude,and the improved watershed algorithm is used to identify and count NEU.The experimental results show that,compared with the gold standard of pathologists,the average accuracy rate of the proposed algorithm for NEU recognition is 94.2%,and the average oversegmentation rate is 2.79%.From the perspective of chromaticity components,the average recognition accuracy of PMN cytoplasmic regions is 81.66%,and the average error rate of MKI value is 0.031%.