Accurate identification and localization of anatomical structures of varying
size and appearance in laparoscopic imaging are necessary to leverage the
potential of computer vision techniques for surgical decision support.
Segmentation performance of such models is traditionally reported using metrics
of overlap such as IoU. However, imbalanced and unrealistic representation of
classes in the training data and suboptimal selection of reported metrics have
the potential to skew nominal segmentation performance and thereby ultimately
limit clinical translation. In this work, we systematically analyze the impact
of class characteristics (i.e., organ size differences), training and test data
composition (i.e., representation of positive and negative examples), and
modeling parameters (i.e., foreground-to-background class weight) on eight
segmentation metrics: accuracy, precision, recall, IoU, F1 score, specificity,
Hausdorff Distance, and Average Symmetric Surface Distance. Based on our
findings, we propose two simple yet effective strategies to improve real-world
applicability of image segmentation models in laparoscopic surgical data: (1)
inclusion of negative examples in the training process and (2) adaptation of
foreground-background weights in segmentation models to maximize model
performance with respect to specific metrics of interest, depending on the
clinical use case.

通过分析类别特征、训练和测试数据的组成以及建模参数对八个分割度量指标的影响，本研究提出了两种简单而有效的策略来改善腹腔镜手术数据中图像分割模型的实际适用性：（1）在训练过程中包含负样本，（2）调整分割模型的前景 - 背景权重以最大化与特定感兴趣度量相对应的模型性能，具体取决于临床用例。