Purpose

In this study, we propose a deep learning approach for assessment of gallbladder (GB) wall vascularity from images of laparoscopic cholecystectomy (LC). Difficulty in the visualization of GB wall vessels may be the result of fatty infiltration or increased thickening of the GB wall, potentially as a result of cholecystitis or other diseases.

Methods

The dataset included 800 patches and 181 region outlines of the GB wall extracted from 53 operations of the Cholec80 video collection. The GB regions and patches were annotated by two expert surgeons using two labeling schemes: 3 classes (low, medium and high vascularity) and 2 classes (low vs. high). Two convolutional neural network (CNN) architectures were investigated. Preprocessing (vessel enhancement) and post-processing (late fusion of CNN output) techniques were applied.

Results

The best model yielded accuracy 94.48% and 83.77% for patch classification into 2 and 3 classes, respectively. For the GB wall regions, the best model yielded accuracy 91.16% (2 classes) and 80.66% (3 classes). The inter-observer agreement was 91.71% (2 classes) and 78.45% (3 classes). Late fusion analysis allowed the computation of spatial probability maps, which provided a visual representation of the probability for each vascularity class across the GB wall region.

Conclusions

This study is the first significant step forward to assess the vascularity of the GB wall from intraoperative images based on computer vision and deep learning techniques. The classification performance of the CNNs was comparable to the agreement of two expert surgeons. The approach may be used for various applications such as for classification of LC operations and context-aware assistance in surgical education and practice.

中文翻译：

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使用深度学习从腹腔镜图像中对胆囊壁血管进行基于补丁的分类

目的

在这项研究中，我们提出了一种从腹腔镜胆囊切除术（LC）图像评估胆囊（GB）壁血管性的深度学习方法。GB壁血管的可视化困难可能是脂肪浸润或GB壁增厚的结果，可能是胆囊炎或其他疾病的结果。

方法

该数据集包括从Cholec80视频集合的53次操作中提取的800个补丁和GB墙的181个区域轮廓。GB区域和斑块由两名专业的外科医生使用两种标记方案进行注释：3类（低，中和高血管）和2类（低与高）。研究了两种卷积神经网络（CNN）架构。应用了预处理（血管增强）和后处理（CNN输出的后期融合）技术。

结果

最好的模型将补丁分类为2类和3类的准确度分别为94.48％和83.77％。对于GB墙区域，最佳模型得出的准确度为91.16％（2个等级）和80.66％（3个等级）。观察员之间的同意率为91.71％（2个等级）和78.45％（3个等级）。后期融合分析允许计算空间概率图，该图提供了GB壁区域上每个血管类别的概率的直观表示。

结论

这项研究是基于计算机视觉和深度学习技术从术中图像评估GB壁血管性迈出的重要的第一步。CNN的分类性能可媲美两名专家外科医生的协议。该方法可用于各种应用，例如用于LC操作的分类以及外科教育和实践中的上下文感知辅助。