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. 2020 Dec;34(12):5377-5383.
doi: 10.1007/s00464-019-07330-8. Epub 2020 Jan 29.

SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology

Sabrina Madad Zadeh  1   2 , Tom Francois  2 , Lilian Calvet  2 , Pauline Chauvet  1   2 , Michel Canis  1   2 , Adrien Bartoli  2 , Nicolas Bourdel  3   4
Affiliations

SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology

Sabrina Madad Zadeh et al. Surg Endosc. 2020 Dec.

Abstract

Background: In laparoscopy, the digital camera offers surgeons the opportunity to receive support from image-guided surgery systems. Such systems require image understanding, the ability for a computer to understand what the laparoscope sees. Image understanding has recently progressed owing to the emergence of artificial intelligence and especially deep learning techniques. However, the state of the art of deep learning in gynaecology only offers image-based detection, reporting the presence or absence of an anatomical structure, without finding its location. A solution to the localisation problem is given by the concept of semantic segmentation, giving the detection and pixel-level location of a structure in an image. The state-of-the-art results in semantic segmentation are achieved by deep learning, whose usage requires a massive amount of annotated data. We propose the first dataset dedicated to this task and the first evaluation of deep learning-based semantic segmentation in gynaecology.

Methods: We used the deep learning method called Mask R-CNN. Our dataset has 461 laparoscopic images manually annotated with three classes: uterus, ovaries and surgical tools. We split our dataset in 361 images to train Mask R-CNN and 100 images to evaluate its performance.

Results: The segmentation accuracy is reported in terms of percentage of overlap between the segmented regions from Mask R-CNN and the manually annotated ones. The accuracy is 84.5%, 29.6% and 54.5% for uterus, ovaries and surgical tools, respectively. An automatic detection of these structures was then inferred from the semantic segmentation results which led to state-of-the-art detection performance, except for the ovaries. Specifically, the detection accuracy is 97%, 24% and 86% for uterus, ovaries and surgical tools, respectively.

Conclusion: Our preliminary results are very promising, given the relatively small size of our initial dataset. The creation of an international surgical database seems essential.

Keywords: Artificial intelligence; Deep learning; Gynaecological surgery; Laparoscopic surgery.

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