New thyroid scintigraphy datasets: Construction and benchmark assessment in diagnosis of residual thyroid tissue

Abstract

Thyroid scintigraphy, a type of single photon emission computed tomography (SPECT) imaging technique that uses radioactive isotopes to capture images of the thyroid gland, helps detect thyroid abnormalities and diagnosing thyroid cancer. A promising research direction for machine learning applications to assist in diagnosis. Most algorithms for detecting and predicting uptake in the thyroid region rely on proprietary or published datasets with unspecified information. This makes comparing the performance among different methods and developing solutions for various problems challenging. To address this issue, we have constructed two standardized datasets of thyroid scintigraphy images for identifying and quantifying the depth. The purpose of designing the models is to establish a benchmark assessment for developing CADx models on the datasets in the future.