Li Yishan, Wang Xiaogang, Gong Qiong, Deng Minghui, Chen Shuchao, Chen Hongbo
School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, China.
Department of Cataract, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
Quant Imaging Med Surg. 2023 Aug 1;13(8):5119-5129. doi: 10.21037/qims-22-1319. Epub 2023 May 25.
With the continuous development of machine vision and imaging technology and its application in computer-aided diagnosis, it is clinically important to use computer technology to assist physicians in accurate cataract surgery. The capsulorhexis directly affects the outcome of cataract surgery, therefore, we design a method to automatically determine the virtual boundary of capsulorhexis for cataract surgery planning and tracking to help surgeons achieve a more ideal capsulotomy geometry.
In this study, an effective method was proposed to detect and display the location of capsulorhexis in cataract videos . The initial step was locating the entire eye area by analyzing the connected components of the mirror reflective points in the image in the cataract surgery video. Then, an operator was designed for ridge edge variation and used to extract pupil edge features. Lastly, circular Hough transform was used to detect the pupillary margin and calculate the boundary between the scleral limbus and the virtual capsulorhexis border in accordance with the pupillary margin and finally displayed it during cataract surgery.
The method was tested on eight videos of cataract surgery and the results showed that 98.52% accuracy was achieved in the localization of the specular reflection point. We compared the proposed operator with the Sobel, Scharr, Laplace and Canny operators and the results showed that our operator achieved the smallest mean square error with the greatest structural similarity.
The analysis demonstrated that the proposed operator outperformed other operators in detection and achieved satisfactory results in the videos of actual cataract surgeries.
随着机器视觉与成像技术的不断发展及其在计算机辅助诊断中的应用，利用计算机技术辅助医生进行精准的白内障手术在临床上具有重要意义。连续环形撕囊直接影响白内障手术的效果，因此，我们设计了一种方法来自动确定连续环形撕囊的虚拟边界，用于白内障手术规划与跟踪，以帮助外科医生实现更理想的囊膜切开形状。
在本研究中，提出了一种有效的方法来检测并显示白内障手术视频中连续环形撕囊的位置。第一步是通过分析白内障手术视频图像中镜面反射点的连通分量来定位整个眼部区域。然后，设计了一个用于脊边缘变化的算子，并用于提取瞳孔边缘特征。最后，使用圆形霍夫变换检测瞳孔边缘，并根据瞳孔边缘计算巩膜缘与虚拟连续环形撕囊边界之间的边界，最终在白内障手术过程中显示出来。
该方法在八个白内障手术视频上进行了测试，结果表明镜面反射点定位的准确率达到了98.52%。我们将所提出的算子与Sobel、Scharr、Laplace和Canny算子进行了比较，结果表明我们的算子实现了最小的均方误差和最大的结构相似性。
分析表明，所提出的算子在检测方面优于其他算子，并在实际白内障手术视频中取得了满意的结果。
