Summary
Objectives: 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.
Conclusion: 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.
Keywords
Optical Coherence Tomography - biopsy guidance - optical soft tissue classification
- identification of pulmonary nodules - Long Short Term Memory - recurrent eural nets
