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Using FastSAM for Creating Custom Automatic Segmentation Models for Medicine and Biology

Year: 2024
Type: conference-paper
Abstract: FastSAM, a publicly available image segmentation model designed for general image segmentation, is turned into a highly adaptable and advanced segmentation tool that requires minimal training in two distinct scenarios. In the first case, we examine macroscopic X-ray images of the knee, in the second case, we focus on microscopic images of the zebra fish embryo retina, which have a significantly smaller spatial scale. We determine the minimum number of images needed to maintain state-of-the-art segmentation quality in each case. Finally, we evaluate the impact of image filtering and the unique considerations of segmenting 3D retinal volumes.
DOI: 10.17979/spudc.9788497498913.34
Authors: Santiago Paramés-Estévez, Diego Pérez-Dones, Ignacio Rego-Pérez, Natividad Oreiro-Villar, Francisco J. Blanco, Javier Roca Pardiñas, Germán González Pazó, David G. Míguez, Alberto P. Muñuzuri
Institutions: Universidade de Santiago de Compostela, Universidade da Coruña, Complexo Hospitalario Universitario A Coruña, Centro de Biología Molecular Severo Ochoa, Instituto de Investigación Biomédica de A Coruña, Universidad Autónoma de Madrid
Language: English
Open Access status: gold
OpenAlex ID: W4407425452
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