Abstract：
BACKGROUND: Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting.
METHODS: Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized.
RESULTS: A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%.
CONCLUSIONS: To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery.
METHODS: Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized.
RESULTS: A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%.
CONCLUSIONS: To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery.
摘要：
背景：高光谱成像（HSI），结合机器学习，可以帮助识别特征组织签名,从而在手术期间实现自动组织识别。这项研究旨在开发第一个基于HSI的自动腹部组织识别，并在预期的双中心环境中使用人体数据。
方法：数据来自2020年9月至2021年6月在两家国际三级转诊医院接受择期开腹手术的患者。在整个外科手术的各个时间点捕获HS图像。所得的RGB图像用13个不同的器官标记进行注释。卷积神经网络(CNN)用于分析，使用外部和内部验证设置。
结果：共纳入169例患者，斯特拉斯堡73人（43.2％），维罗纳96人（56.8％）。中心内部验证将两个中心的患者合并为一个队列，随机分配到培训中(127名患者，75.1%,585张图像）和测试集（42名患者，24.9%,181图像）。此验证设置显示最佳性能。皮肤（100％）和肝脏（97％）的真实阳性率最高。错误分类包括具有相似胚胎起源的组织（网膜和肠系膜：32％）或具有重叠边界的组织（肝脏和肝韧带：22％）。十个组织类别的中值DICE评分超过80%。
结论：为了改善自动手术场景分割并推动临床转化，多中心准确的恒生指数数据集是必不可少的，但需要进一步的工作来量化HSI的临床价值.恒生指数可能被纳入一门新的组学科学，即手术验光术，在手术过程中使用光提取可量化的组织特征。
方法：数据来自2020年9月至2021年6月在两家国际三级转诊医院接受择期开腹手术的患者。在整个外科手术的各个时间点捕获HS图像。所得的RGB图像用13个不同的器官标记进行注释。卷积神经网络(CNN)用于分析，使用外部和内部验证设置。
结果：共纳入169例患者，斯特拉斯堡73人（43.2％），维罗纳96人（56.8％）。中心内部验证将两个中心的患者合并为一个队列，随机分配到培训中(127名患者，75.1%,585张图像）和测试集（42名患者，24.9%,181图像）。此验证设置显示最佳性能。皮肤（100％）和肝脏（97％）的真实阳性率最高。错误分类包括具有相似胚胎起源的组织（网膜和肠系膜：32％）或具有重叠边界的组织（肝脏和肝韧带：22％）。十个组织类别的中值DICE评分超过80%。
结论：为了改善自动手术场景分割并推动临床转化，多中心准确的恒生指数数据集是必不可少的，但需要进一步的工作来量化HSI的临床价值.恒生指数可能被纳入一门新的组学科学，即手术验光术，在手术过程中使用光提取可量化的组织特征。
