Methods Inf Med - Investigating recurrent neural networks for OCT A-scan based tissue analysis.

Tópicos

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Resumo

JECTIVES: 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.

Resumo Limpo

jectiv optic coher tomographi oct propos high resolut imag modal guid transbronchi biopsi studi address question whether individu ascan obtain needl direct can contribut identif pulmonari nodulesmethod oct ascan fresh resect human lung tissu specimen record custom needl embed optic fiber bidirect long short term memori network blstms train random distribut train test set acquir ascan patient specif train differ preprocess step evaluatedresult classif rate archiv differ train scenario sensit specif highest patient specif train low pass filter decreas accuraci refer distribut higher cutoff frequenc lower cutoff frequenciesconclus result indic grey valu base classif feasibl may provid addit inform diagnosi navig furthermor experi show patient specif signal properti indic lower upper part frequenc spectrum contribut classif

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