Inception Capsule Network for Retinal Blood Vessel Segmentation and Centerline Extraction

Kromm, C.; Rohr, K.

doi:10.1101/815555

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bioRxiv · 10.1101/815555

Inception Capsule Network for Retinal Blood Vessel Segmentation and Centerline Extraction

Kromm, C.·Rohr, K.

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Abstract

Automatic segmentation and centerline extraction of retinal blood vessels from fundus image data is crucial for early detection of retinal diseases. We have developed a novel deep learning method for segmentation and centerline extraction of retinal blood vessels which is based on the Capsule network in combination with the Inception architecture. Compared to state-of-the-art deep convolutional neural networks, our method has much fewer parameters due to its shallow architecture and generalizes well without using data augmentation. We performed a quantitative evaluation using the DRIVE dataset for both vessel segmentation and centerline extraction. Our method achieved state-of-the-art performance for vessel segmentation and outperformed existing methods for centerline extraction.

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Kromm, C., Rohr, K.. 2019-10-22. Inception Capsule Network for Retinal Blood Vessel Segmentation and Centerline Extraction. https://doi.org/10.1101/815555

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