Multi-Pathology Segmentation of the Lumbar Spine - Leite, Claudio; Almeida, Jurandy (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
The diagnosis of spinal pathologies is complex due to the frequent overlap of multiple diseases in the same anatomical location, a scenario that current segmentation or classification methods do not efficiently address. This work presents an empirical study on the segmentation of multiple overlapping pathologies, proposing and systematically comparing three strategies: (i) a baseline binary class approach using independent models; (ii) a multi-class approach mapping disease combinations to unique labels; and (iii) a multi-label approach using parallel channels to explicitly model co-occurrence.We evaluated over 300 training and inference pipelines, combining five neural network architectures and three loss functions. Our preliminary results show that the multi-label strategy significantly outperforms the other approaches in both accuracy and computational efficiency, establishing a promising direction for developing robust, scalable diagnostic tools.