2018/01/01 by André Klein, Jan Warszawski, Jens Hillengaß +2
Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial intelligence #Computer science #Computer vision #Data set #Engineering #Image segmentation #Medical Imaging and Analysis #Medicine #Pattern recognition (psychology) #Radiology #Radiomics and Machine Learning in Medical Imaging #Segmentation #Task (project management) #cs.CV
paper · pdf · doi:10.1007/978-3-662-56537-7_59
Accepted conference paper at BVM 2018
openalex publication_date 2018/01/01 · arxiv created 2018/04/03 · arxiv updated 2018/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Bone segmentation from CT images is a task that has been worked on for decades. It is an important ingredient to several diagnostics or treatment planning approaches and relevant to various diseases. As high-quality manual and semi-automatic bone segmentation is very time-consuming, a reliable and fully automatic approach would be of great interest in many scenarios. In this publication, we propose a UNet inspired architecture to address the task using Deep Learning. We evaluated the approach on whole-body CT scans of patients suffering from multiple myeloma. As the disease decomposes the bone, an accurate segmentation is of utmost importance for the evaluation of bone density, disease staging and localization of focal lesions. The method was evaluated on an in-house data-set of 6000 2D image slices taken from 15 whole-body CT scans, achieving a dice score of 0.96 and an IOU of 0.94.