title: Investigating recurrent neural networks for OCT A-scan based tissue analysis.
abstract: OBJECTIVE
  Optical Coherence Tomography (OCT) has been proposed as a high resolution image modality to guide transbronchial biopsies. In this study we address the question, whether individual A-scans obtained in needle direction can contribute to the identification of pulmonary nodules.
  
  
  METHODS
  OCT A-scans from freshly resected human lung tissue specimen were recorded through a customized needle with an embedded optical fiber. Bidirectional Long Short Term Memory networks (BLSTMs) were trained on randomly distributed training and test sets of the acquired A-scans. Patient specific training and different pre-processing steps were evaluated.
  
  
  RESULTS
  Classification rates from 67.5% up to 76% were archived for different training scenarios. Sensitivity and specificity were highest for a patient specific training with 0.87 and 0.85. Low pass filtering decreased the accuracy from 73.2% on a reference distribution to 62.2% for higher cutoff frequencies and to 56% for lower cutoff frequencies.
  
  
  CONCLUSIONS
  The results indicate that a grey value based classification is feasible and may provide additional information for diagnosis and navigation. Furthermore, the experiments show patient specific signal properties and indicate that the lower and upper parts of the frequency spectrum contribute to the classification.
authors.affiliation: C. Otte, TU Hamburg-Harburg, Schwarzenbergstr. 95 E, room 3.088, 21073 Hamburg, Germany, E-mail: christoph.otte@tuhh.de.
authors.forename: C
authors.initials: C
authors.lastname: Otte
authors.forename: S
authors.initials: S
authors.lastname: Otte
authors.forename: L
authors.initials: L
authors.lastname: Wittig
authors.forename: G
authors.initials: G
authors.lastname: Hüttmann
authors.forename: C
authors.initials: C
authors.lastname: Kugler
authors.forename: D
authors.initials: D
authors.lastname: Drömann
authors.forename: A
authors.initials: A
authors.lastname: Zell
authors.forename: A
authors.initials: A
authors.lastname: Schlaefer
mesh_terms: D001707:Biopsy, Needle; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D061705:Image-Guided Biopsy; D008168:Lung / Q000473:pathology*; D055613:Multiple Pulmonary Nodules / Q000145:classification / Q000473:pathology; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012984:Software; D041623:Tomography, Optical Coherence
keywords: Long Short Term Memory; Optical Coherence Tomography; biopsy guidance; identification of pulmonary nodules; optical soft tissue classification; recurrent eural nets