目的：建立基于红外图像的瞳孔直径测量算法，以便在实际临床环境中使用。

方法：纳入2022-09/12于沈阳何氏眼科医院门诊患者188例，共收集红外瞳孔图像13 470张。所有用于瞳孔分割的红外图像均使用Labelme软件进行标注。瞳孔直径的计算分为四个步骤：图像预处理、瞳孔识别与定位、瞳孔分割及直径计算。计算过程中使用修改后的YoloV3模型和DeeplabV3+模型，这两个模型需要事先训练。

结果：测试数据集共1 348张红外瞳孔图像，修改后的YoloV3模型的检测率为99.98%，瞳孔的平均精确度(AP)为0.80。DeeplabV3+模型达到了99.23%的背景交并比(IOU)，93.81%的瞳孔IOU和平均96.52%的IOU。测试数据集中瞳孔直径范围为20至56像素，平均为36.06±6.85像素。预测和实际值之间瞳孔直径的绝对误差范围为0至7像素，平均绝对误差(MAE)为1.06±0.96像素。

结论：本研究成功展示了一种基于红外图像的稳健瞳孔直径测量算法，证明该算法具有高度准确性和可靠性，适用于临床应用。

METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included, and 13 470 infrared pupil images were collected for the study. All infrared images for pupil segmentation were labeled using the Labelme software. The computation of pupil diameter is divided into four steps: image pre-processing, pupil identification and localization, pupil segmentation, and diameter calculation. Two major models are used in the computation process: the modified YoloV3 and Deeplabv3+ models, which must be trained beforehand.

RESULTS:The test dataset included 1 348 infrared pupil images. On the test dataset, the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils. The DeeplabV3+ model achieved a background intersection over union(IOU)of 99.23%, a pupil IOU of 93.81%, and a mean IOU of 96.52%. The pupil diameters in the test dataset ranged from 20 to 56 pixels, with a mean of 36.06±6.85 pixels. The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels, with a mean absolute error(MAE)of 1.06±0.96 pixels.

CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm, proven to be highly accurate and reliable for clinical application.