Abstract
Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
Highlights
Recent research shows that eye pathologies contribute to more than 280 million visual impairments [1]
We propose two methods, corresponding to mechanisms of manually feature exaction and automatically feature exaction, to tackle difficulties for the needle segmentation in Optical Coherence Tomography (OCT) images: (a) with the needle shadow principle [22], a conventional method based on morphological features (MF) comes to the mind; (b) another approach is based on the recently developed fully convolution neural networks (FCN) [23], which has been applied for MRI medical image analysis
We studied the first step of obtaining the needle point cloud for needle pose and position estimation: the needle segmentation in OCT images
Summary
Recent research shows that eye pathologies contribute to more than 280 million visual impairments [1]. The incisions, created by keratome and trocar at the sclera in a circle and 3.5 mm away from the limbus [2], are made to provide the entrance for three tools: light source, surgical tool, and irrigation cannula [3,4]. The light source is used to illuminate the intended area on the retina, allowing the planar view of the area obtained and analyzed by surgeons through the microscope. To address these challenges, the surgical progress proposes a great challenge of delicate operation and sensitive perception to surgeons. The surgical instrument segmentation is the first step to estimate the needle pose and position, which is extremely important to enhance surgeons’
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