Investigating recurrent neural networks for OCT A-scan based tissue analysis
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Investigating recurrent neural networks for OCT A-scan based tissue analysis. / Otte, C; Otte, S; Wittig, L; Hüttmann, G; Kugler, C; Drömann, D; Zell, A; Schlaefer, A.
in: METHOD INFORM MED, Jahrgang 53, Nr. 4, 2014, S. 245-9.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Investigating recurrent neural networks for OCT A-scan based tissue analysis
AU - Otte, C
AU - Otte, S
AU - Wittig, L
AU - Hüttmann, G
AU - Kugler, C
AU - Drömann, D
AU - Zell, A
AU - Schlaefer, A
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Biopsy, Needle
KW - Humans
KW - Image Interpretation, Computer-Assisted
KW - Image-Guided Biopsy
KW - Lung/pathology
KW - Multiple Pulmonary Nodules/classification
KW - Neural Networks, Computer
KW - Sensitivity and Specificity
KW - Software
KW - Tomography, Optical Coherence
U2 - 10.3414/ME13-01-0135
DO - 10.3414/ME13-01-0135
M3 - SCORING: Journal article
C2 - 24992968
VL - 53
SP - 245
EP - 249
JO - METHOD INFORM MED
JF - METHOD INFORM MED
SN - 0026-1270
IS - 4
ER -