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SVPath: A Deep Learning Tool for Analysis of Stria Vascularis from Histology Slides.
Jain, Aseem; Perdomo, Dianela; Nagururu, Nimesh; Li, Jintong Alice; Ward, Bryan K; Lauer, Amanda M; Creighton, Francis X.
Afiliação
  • Jain A; College of Medicine, University of Cincinnati, 231 Albert Sabin Way, Cincinnati, OH, 45267, USA. jain2ae@mail.uc.edu.
  • Perdomo D; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Nagururu N; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Li JA; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Ward BK; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Lauer AM; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Creighton FX; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
J Assoc Res Otolaryngol ; 25(4): 1-8, 2024 Aug.
Article em En | MEDLINE | ID: mdl-38760547
ABSTRACT

INTRODUCTION:

The stria vascularis (SV) may have a significant role in various otologic pathologies. Currently, researchers manually segment and analyze the stria vascularis to measure structural atrophy. Our group developed a tool, SVPath, that uses deep learning to extract and analyze the stria vascularis and its associated capillary bed from whole temporal bone histopathology slides (TBS).

METHODS:

This study used an internal dataset of 203 digitized hematoxylin and eosin-stained sections from a normal macaque ear and a separate external validation set of 10 sections from another normal macaque ear. SVPath employed deep learning methods YOLOv8 and nnUnet to detect and segment the SV features from TBS, respectively. The results from this process were analyzed with the SV Analysis Tool (SVAT) to measure SV capillaries and features related to SV morphology, including width, area, and cell count. Once the model was developed, both YOLOv8 and nnUnet were validated on external and internal datasets.

RESULTS:

YOLOv8 implementation achieved over 90% accuracy for cochlea and SV detection. nnUnet SV segmentation achieved a DICE score of 0.84-0.95; the capillary bed DICE score was 0.75-0.88. SVAT was applied to compare both the ears used in the study. There was no statistical difference in SV width, SV area, and average area of capillary between the two ears. There was a statistical difference between the two ears for the cell count per SV.

CONCLUSION:

The proposed method accurately and efficiently analyzes the SV from temporal histopathology bone slides, creating a platform for researchers to understand the function of the SV further.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estria Vascular / Aprendizado Profundo Limite: Animals Idioma: En Revista: J Assoc Res Otolaryngol Assunto da revista: OTORRINOLARINGOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estria Vascular / Aprendizado Profundo Limite: Animals Idioma: En Revista: J Assoc Res Otolaryngol Assunto da revista: OTORRINOLARINGOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos