A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images

TL;DR

This paper introduces a multiple-instance learning method using variational Bayesian inference to assess gallbladder vascularity from laparoscopic images, achieving high accuracy without manual patch labeling.

Contribution

It proposes a novel MIL approach for gallbladder vascularity assessment that outperforms existing methods and eliminates the need for manual instance labeling.

Findings

Achieved 92.1% accuracy in image-based classification.

Achieved 90.3% accuracy in patient-based classification.

Outperformed state-of-the-art MIL and single-instance methods.

Abstract

An important task at the onset of a laparoscopic cholecystectomy (LC) operation is the inspection of gallbladder (GB) to evaluate the thickness of its wall, presence of inflammation and extent of fat. Difficulty in visualization of the GB wall vessels may be due to the previous factors, potentially as a result of chronic inflammation or other diseases. In this paper we propose a multiple-instance learning (MIL) technique for assessment of the GB wall vascularity via computer-vision analysis of images from LC operations. The bags correspond to a labeled (low vs. high) vascularity dataset of 181 GB images, from 53 operations. The instances correspond to unlabeled patches extracted from these images. Each patch is represented by a vector with color, texture and statistical features. We compare various state-of-the-art MIL and single-instance learning approaches, as well as a proposed MIL technique based on variational Bayesian inference. The methods were compared for two experimental tasks: image-based and video-based (i.e. patient-based) classification. The proposed approach presents the best performance with accuracy 92.1% and 90.3% for the first and second task, respectively. A significant advantage of the proposed technique is that it does not require the time-consuming task of manual labelling the instances.