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SurgAI3.8K: A Labeled Dataset of Gynecologic Organs in Laparoscopy with Application to Automatic Augmented Reality
Sabrina Madad Zadeh1, Tom François2, Aurélie Comptour3
1Surgical Oncology Department, Centre Jean Perrin (Dr. Zadeh), Clermont-Ferrand, France; EnCoV, Institut Pascal, UMR CNRS/Université Clermont-Auvergne (Drs. Zadeh, François, Canis, Bourdel, Bartoli), Clermont-Ferrand, France.
Study Objective:
We focus on explaining the concepts underlying artificial intelligence (AI), using Uteraug, a laparoscopic surgery guidance application based on Augmented Reality (AR), to provide concrete examples. AI can be used to automatically interpret the surgical images. We are specifically interested in the tasks of uterus segmentation and uterus contouring in laparoscopic images. A major difficulty with AI methods is their requirement for a massive amount of annotated data. We propose SurgAI3.8K, the first gynaecological dataset with annotated anatomy. We study the impact of AI on automating key steps of Uteraug.
Design:
We constructed the SurgAI3.8K dataset with 3800 images extracted from 79 laparoscopy videos. We created the following annotations: the uterus segmentation, the uterus contours and the regions of the left and right fallopian tube junctions. We divided our dataset into a training and a test dataset. Our engineers trained a neural network from the training dataset. We then investigated the performance of the neural network compared to the experts on the test dataset. In particular, we established the relationship between the size of the training dataset and the performance, by creating size-performance graphs.
Setting:
University.
Patients:
Not available.
Intervention:
Not available.
Measurements And Main Results:
The size-performance graphs show a performance plateau at 700 images for uterus segmentation and 2000 images for uterus contouring. The final segmentation scores on the training and test dataset were 94.6% and 84.9% (the higher, the better) and the final contour error were 19.5% and 47.3% (the lower, the better). These results allowed us to bootstrap Uteraug, achieving AR performance equivalent to its current manual setup.
Conclusion:
We describe a concrete AI system in laparoscopic surgery with all steps from data collection, data annotation, neural network training, performance evaluation, to final application.
