MedYOLO: A Medical Image Object Detection Framework
Joseph Sobek Jose R. Medina Inojosa Betsy J. Medina Inojosa S. M. Rassoulinejad-Mousavi Gian Marco Conte Francisco Lopez-Jimenez Bradley J. Erickson
Artificial intelligence-enhanced identification of organs, lesions, and other
structures in medical imaging is typically done using convolutional neural
networks (CNNs) designed to make voxel-accurate segmentations of the region of
interest. However, the labels required to train these CNNs are time-consuming
to generate and require attention from subject matter experts to ensure
quality. For tasks where voxel-level precision is not required, object
detection models offer a viable alternative that can reduce annotation effort.
Despite this potential application, there are few options for general purpose
object detection frameworks available for 3-D medical imaging. We report on
MedYOLO, a 3-D object detection framework using the one-shot detection method
of the YOLO family of models and designed for use with medical imaging. We
tested this model on four different datasets: BRaTS, LIDC, an abdominal organ
Computed Tomography (CT) dataset, and an ECG-gated heart CT dataset. We found
our models achieve high performance on commonly present medium and large-sized
structures such as the heart, liver, and pancreas even without hyperparameter
tuning. However, the models struggle with very small or rarely present
structures.