Multiscale Optical Imaging Fusion for Cervical Precancer Diagnosis: Integrating Widefield Colposcopy and High-Resolution Endomicroscopy

David Brenes, Mila P. Salcedo, Jackson B. Coole, Yajur Maker, Alex Kortum, Richard A. Schwarz, Jennifer Carns, Imran S. Vohra, Julio C. Possati-Resende, Marcio Antoniazzi, Bruno de Oliveira Fonseca, Karen C.Borba Souza, Iara V.Vidigal Santana, Flavia Fazzio Barbin, Regis Kreitchmann, Nirmala Ramanujam, Kathleen M. Schmeler, Rebecca Richards-Kortum

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Early detection and treatment of cervical precancers can prevent disease progression. However, in low-resource communities with a high incidence of cervical cancer, high equipment costs and a shortage of specialists hinder preventative strategies. This manuscript presents a low-cost multiscale in vivo optical imaging system coupled with a computer-aided diagnostic system that could enable accurate, real-time diagnosis of high-grade cervical precancers. Methods: The system combines portable colposcopy and high-resolution endomicroscopy (HRME) to acquire spatially registered widefield and microscopy videos. A multiscale imaging fusion network (MSFN) was developed to identify cervical intraepithelial neoplasia grade 2 or more severe (CIN 2+). The MSFN automatically identifies and segments the ectocervix and lesions from colposcopy images, extracts nuclear morphology features from HRME videos, and integrates the colposcopy and HRME information. Results: With a threshold value set to achieve sensitivity equal to clinical impression (0.98 [p = 1.0]), the MSFN achieved a significantly higher specificity than clinical impression (0.75 vs. 0.43, p = 0.000006). Conclusion: Our findings show that multiscale optical imaging of the cervix allows the highly sensitive and specific detection of high-grade precancers. Significance: The multiscale imaging system and MSFN could facilitate the accurate, real-time diagnosis of cervical precancers in low-resource settings.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
DOIs
StateAccepted/In press - 2024

Keywords

  • Biopsy
  • Cervical cancer
  • cervical cancer
  • deep learning
  • Image segmentation
  • Imaging
  • Lesions
  • machine learning
  • multi-modality fusion
  • Optical imaging
  • Optical imaging
  • Videos

ASJC Scopus subject areas

  • Biomedical Engineering

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