Multi-Pathology Segmentation of the Lumbar Spine - Leite, Claudio; Almeida, Jurandy (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2025.38301, 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.