Percutaneous renal access is the critical initial step in many medical
settings. In order to obtain the best surgical outcome with minimum
patient morbidity, an improved method for access to the renal calyx is
needed. In our study, we built a forward-view optical coherence
tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN)
guidance. Porcine kidneys were imaged in our experiment to demonstrate
the feasibility of the imaging system. Three tissue types of porcine
kidneys (renal cortex, medulla, and calyx) can be clearly
distinguished due to the morphological and tissue differences from the
OCT endoscopic images. To further improve the guidance efficacy and
reduce the learning burden of the clinical doctors, a
deep-learning-based computer aided diagnosis platform was developed to
automatically classify the OCT images by the renal tissue types.
Convolutional neural networks (CNN) were developed with labeled OCT
images based on the ResNet34, MobileNetv2 and ResNet50 architectures.
Nested cross-validation and testing was used to benchmark the
classification performance with uncertainty quantification over 10
kidneys, which demonstrated robust performance over substantial
biological variability among kidneys. ResNet50-based CNN models
achieved an average classification accuracy of
82.6%±3.0%. The classification precisions were
79%±4% for cortex, 85%±6%
for medulla, and 91%±5% for calyx and the
classification recalls were 68%±11% for cortex,
91%±4% for medulla, and
89%±3% for calyx. Interpretation of the CNN
predictions showed the discriminative characteristics in the OCT
images of the three renal tissue types. The results validated the
technical feasibility of using this novel imaging platform to
automatically recognize the images of renal tissue structures ahead of
the PCN needle in PCN surgery.
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