FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI

Lower back pain is often related to spinal diseases. In particular, Vertebral Compression Fractures (VCFs) can impair mobility and compromise quality of life. In a Computer-Aided Diagnosis (CAD) context, the segmentation of VCFs is a challenging task due to non-homogeneous intensities within the same vertebral body. Semiautomatic segmentation methods have been employed to cope with this challenge. However, these methods require inside and outside annotation, which is not practical when analyzing a more significant number of exams. Aimed at minimizing the time spent on manual annotation, we proposed Fast INside Estimation (FINE), which automatically estimates the inside seeds based on the outside seeds. The experimental results with a representative dataset showed that FINE does not demand manual inside annotation, what the competitors methods do, and achieve higher Recall and Dice Score, on average, 97% and 96%, respectively. Higher Recall is particularly essential on features extraction and classification of VCFs. Therefore, FINE speeds up the manual annotation process while allowing more accurate semiautomatic segmentation.