Paramés-Estévez, SantiagoPérez-Dones, DiegoRego-Pérez, I.Oreiro Villar, NatividadBlanco García, Francisco JRoca-Pardiñas, JavierGonzález Pazó, GermánMíguez, David G.Muñuzuri, Alberto P.2025-01-292025-01-292024-12-13Paramés-Estévez S, Pérez-Dones D, Rego-Pérez I, Oreiro-Villar N, Blanco FJ, Roca Pardiñas J, González Pazó G, Míguez DG, Muñuzuri AP. Custom automatic segmentation models for medicine and biology based on FastSAM. WESEAS Trans Biol Biomed. 2024;21:373-384.2224-2902http://hdl.handle.net/2183/40937[Abstract] FastSAM, a public image segmentation model trained on everyday images, is used to achieve a customizable and state-of-the-art segmentation model minimizing the training in two completely different scenarios. In one example we consider macroscopic X-ray images of the knee area. In the second example, images were acquired by microscopy of the volumetric zebrafish embryo retina with a much smaller spatial scale. In both cases, we analyze the minimum set of images required to segmentate keeping the state-of-the-art standards. The effect of filters on the pictures and the specificities of considering a 3D volume for the retina images are also analyzed.engAutomatic segmentationFastSAMX-ray imagesMicroscopy imagesLow-Resource FriendlyGeneralizable approachjournal articleopen access10.37394/23208.2024.21.38