Neonatal Face Segmentation with and without Clinical Devices using SAM

  • Pedro Henrique Silva Domingues FEI
  • Tatiany Marcondes Heiderich FEI / UNIFESP
  • Marina Carvalho de Moraes Barros UNIFESP
  • Ruth Guinsburg UNIFESP
  • Carlos Eduardo Thomaz FEI


Facial expression analysis has been widely used as one of the main approaches for pain diagnosis, both by humans and computing systems. However, in clinical practice, newborns who remain hospitalized in Neonatal Intensive Care Units often have devices connected to their faces, such as enteral/gastric probes, orotracheal intubation tubes, and phototherapy goggles, which hinder the visualization of facial regions making the proper diagnosis of pain much harder in practice. Therefore, to address this issue, we have evaluated the state-of-the-art Segment Anything Model (SAM) tool combined with RetinaFace for segmentation of 2D face images of neonates, including free faces and the ones with devices connected, against a simple and traditional landmark method, and a recently proposed deep neural network fine-tuned for face segmentation under occlusion. SAM performed comparatively better than the other two models for both no occlusion and high occlusion 2D face images, scoring on average impressively 0.98 and 0.91 at the standard dice similarity coefficient respectively.


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DOMINGUES, Pedro Henrique Silva; HEIDERICH, Tatiany Marcondes; BARROS, Marina Carvalho de Moraes; GUINSBURG, Ruth; THOMAZ, Carlos Eduardo. Neonatal Face Segmentation with and without Clinical Devices using SAM. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 101-104. DOI:

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