AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols.

Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study.

Objectives: The objectives of the study are (1) to distinguish lipoma (L) from atypical lipomatous tumor (ALT) using MRI qualitative features, (2) to assess the value of contrast enhancement, and (3) to evaluate the reproducibility and confidence level of radiological readings.

Materials And Methods: Patients with pathologically proven L or ALT, who underwent MRI within 3 months from surgical excision were included in this retrospective multicenter international study. Two radiologists independently reviewed MRI centrally.