Fully automatic estimation of the angular distribution of the waist of the nerve fiber layer in the optic nerve head

• In this paper, an automatic strategy for measuring the thickness of the nerve fiber layer around the optic nerve head is proposed. The strategy presented uses two independent 2D U-nets that each perform a segmentation task. One network learns to segment the vitreous body in standard Cartesian image domain and the second learns to segment a disc around a point of interest in a polar image domain. The output from the neural networks are then combined to find the thickness of the waist of the nerve fiber layer as a function of the angle around the center of the optic nerve head in the frontal plane. The two networks are trained with a combined data set that has been captured on two separate OCT systems (spectral domain Topcon OCT 2000 and swept source Topcon OCT Triton) which have been annotated with a semi-automatic algorithm by up to 3 annotators. Initial results show that the automatic algorithm produces results that are comparable to the results from the semi-automatic algorithm used for reference, in a fraction of the time, independent of the annotator. The automatic algorithm has the potential to replace the semi-automatic algorithm and opens the possibility for clinical routine estimation of the nerve fiber layer. This would in turn allow the detection of loss of nerve fiber layer earlier than before which is anticipated to be important for detection of glaucoma.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Oftalmologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Ophthalmology (hsv//eng)

Nyckelord

Deep learning

Glaucoma

Image segmentation

Ophthalmology

Publikations- och innehållstyp

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