深度学习的高光谱技术鉴定了口服鳞状细胞癌中的转移性淋巴结
Deep learning-based hyperspectral technique identifies metastatic lymph nodes in oral squamous cell carcinoma-A pilot study
影响因子:2.90000
分区:医学3区 / 牙科与口腔外科4区
发表日期:2025 Feb
作者:
Qingxiang Li, Xueyu Zhang, Jianyun Zhang, Hongyuan Huang, Liangliang Li, Chuanbin Guo, Wei Li, Yuxing Guo
摘要
为了建立一个基于高光谱成像和深度学习的系统，以检测转移性淋巴结中的癌细胞。收集了45个口服鳞状细胞癌（OSCC）患者的转移性淋巴结的连续切片。建立了改进的重置算法，以深入学习，以分析癌细胞和淋巴细胞之间的光谱曲线差异，并且在肿瘤组织和正常组织之间。发现癌细胞，淋巴细胞，淋巴细胞和红细胞在转移性淋巴结中的平均度和平均透明度的准确性（均为80）的准确性（均为80均准确性（A） （AA）为85.46％。高光谱图像和深度学习可以识别癌症区域，并且联合（IOU）和准确性的平均相交分别为0.6253和0.7692。这表明，基于深度学习的高光谱技术可以识别OSCC转移性淋巴结中的高度准确性，从而可以识别出高准确性的工作诊断，并提高工作效率的高度准确性。但是这些是初步结果，仅限于一小部分样本。
Abstract
To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue.It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively.This study indicated that deep learning-based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample.