Título: MRI LUMBAR SPINE SEMANTIC SEGMENTATION USING YOLOv8
Autores: Fabio Rodrigo Fernandes de Oliveira, Luiz Alberto Pinto, Gustavo Maia de Almeida, Daniel Cruz Cavalieri
Resumo: The use of Artificial Intelligence (AI) as an assistant for diagnosis in imaging exams has already proven to be effective, and is known as Computer-Aided Diagnosis (CAD). This paper evaluates the effectiveness of using a single network, YOLOv8x is the current state-of-the-art in the YOLO family, for lumbar spine detection and segmentation in Magnetic Resonance Imaging (MRI) exams. The network was used for detection, classification, and semantic segmentation, generating the masks over the vertebrae, which simplified the implementation and reduced the computational cost. Encouraging results were obtained using a dataset of 1,116 samples (images). The detection step achieved a mean average precision (mAP) of 0.989 at 50% intersection over union (IoU), mAP:50-95 of 0.886, recall of 0.98, and precision of 0.97. For bounding box marking, the following results were achieved: mAP of 0.978 at 50% IoU, mAP:50-95 of 0.882, recall of 0.971, and precision of 0.948. The semantic segmentation step achieved a mAP of 0.978 at 50% IoU, mAP:50-95 of 0.856, recall of 0.971, and precision of 0.948. These results demonstrate the effectiveness of using YOLOv8x for lumbar spine detection and segmentation in MRI exams
Palavras-chave: CAD, Semantic Segmentation, DeepLearning, YOLOv8x, Lumbar Spine, MRI
Páginas: 8
Código DOI: 10.21528/CBIC2023-103
Artigo em pdf: CBIC_2023_paper103.pdf
Arquivo BibTeX: CBIC_2023_103.bib