Automatic Thyroid Scintigram Segmentation using U-Net

Abstract

Thyroid scintigraphy is an important tool to determine thyroid function and pathologies. The manual segmentation of these images is a time-intensive and error-prone task required to evaluate the scintigram. In this paper, a 5-layer U-Net is presented that automatically detects and evaluates thyroids in scintigrams by segmenting the left and right lobe and calculating the uptake used for diagnosis. The dataset used to train the network contains 2 734 different thyroid scintigrams collected over the course of four years from a medical office. The network reaches a median Dice score of 0.921 for the thyroid lobes and 0.937 for the complete thyroid, while maintaining a median difference of 3.520 cm2 for the size of the thyroid and 0.029 percentage points for the uptake. Overall, the trained network has the potential to speed up the diagnostic process, while improving the consistency and accuracy of medical diagnoses of thyroid scintigrams.