Authors:
Constantinos Loukas
1
and
Dimitrios Schizas
2
Affiliations:
1
Medical Physics Lab, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75 str., Athens, Greece
;
2
1st Department of Surgery, Laikon General Hospital, National and Kapodistrian University of Athens, Athens, Greece
Keyword(s):
Surgery, Laparoscopic Cholecystectomy, Gallbladder, Vascularity, Classification, Cnn, Deep Learning.
Abstract:
Despite the significant progress in content-based video analysis of surgical procedures, methods on analyzing still images acquired during the operation are limited. In this paper we elaborate on a novel idea for computer vision-based assessment of the vascularity of the gallbladder (GB) wall, using frames extracted from videos of laparoscopic cholecystectomy. The motivation was based on the fact that the wall’s vascular pattern provides an indirect indication of the GB condition (e.g. fat coverage, wall thickening, inflammation), which in turn is usually related to the operation complexity. As the GB wall vascularity may appear irregular, in this study we focus on the classification of rectangular sub-regions (patches). A convolutional neural network (CNN) is proposed for patch classification based on two ground-truth annotation schemes: 3-classes (Low, Medium and High vascularity) and 2-classes (Low vs. High). Moreover, we employed three popular classifiers with a rich set of hand-
crafted descriptors. The CNN achieved the best performance with accuracy: 98% and 83.1%, and mean F1-score: 98% and 80.4%, for 2-class and 3-class classification, respectively. The other methods’ performance was lower by 2%-6% (2-classes) and 6%-17% (3 classes). Our results indicate that CNN-based patch classification is promising for intraoperative assessment of the GB wall vascularity.
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