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Organ Finder – A New AI-based Organ Segmentation Tool for CT

Author(s): Edenbrandt L, Enqvist O, Larsson M, Ulen J

Background: Automated organ segmentation in Computed Tomography (CT) is a vital component in many artificial intelligence-based tools in medical imaging. This study presents a new organ segmentation tool called Organ Finder 2.0. In contrast to most existing methods, Organ Finder was trained and evaluated on a rich multi- origin dataset with both contrast and non-contrast studies from different vendors and patient populations.

Approach: A total of 1,171 CT studies from seven different publicly available CT databases were retrospectively included. Twenty CT studies were used as test set and the remaining 1,151 were used to train a convolutional neural network. Twenty-two different organs were studied. Professional annotators segmented a total of 5,826 organs and segmentation quality was assured manually for each of these organs.

Results: Organ Finder showed high agreement with manual segmentations in the test set. The average Dice index over all organs was 0.93 and the same high performance was found for four different subgroups of the test set based on the presence or absence of intravenous and oral contrast.

Conclusions: An AI-based tool can be used to accurately segment organs in both contrast and non-contrast CT studies. The results indicate that a large training set and high-quality manual segmentations should be used to handle common variations in the appearance of clinical CT studies.

Journal Statistics

Impact Factor: * 3.7

CiteScore: 2.9

Acceptance Rate: 11.01%

Time to first decision: 10.4 days

Time from article received to acceptance: 2-3 weeks

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