Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training.

BACKGROUND

Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle, making the lesions difficult to detect. Second-observer is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence (AI).

AIM

To achieve appropriate AI model training, a large annotated dataset is necessary to train the AI models. Our goal in this research is to compare two methods for decreasing the annotation time to establish ground truth: Skip-slice annotation and AI-initiated annotation.

METHODS

We developed a 2D U-Net as an AI second observer for detecting colorectal cancer (CRC) and an ensemble of 5 differently initiated 2D U-Net for ensemble technique. Each model was trained with 51 cases of annotated CRC computed tomography of the abdomen and pelvis, tested with 7 cases, and validated with 20 cases from The Cancer Imaging Archive cases. The sensitivity, false positives per case, and estimated Dice coefficient were obtained for each method of training. We compared the two methods of annotations and the time reduction associated with the technique. The time differences were tested using Friedman's two-way analysis of variance.

RESULTS

Sparse annotation significantly reduces the time for annotation particularly skipping 2 slices at a time (

CONCLUSION

Our data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth.

Informations de copyright

©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.