## 1 Introduction  
Adaptive optics optical coherence tomography (AO-OCT) imaging is a non-invasive technique that provides improved lateral resolution compared to traditional optical coherence tomography (OCT) by using adaptive optics (AO) to correct for ocular aberrations. With AO-OCT, it is now possible to obtain three-dimensional (3D), cellular-level resolution of the retina and optic nerve head to study ocular physiology and diseases [1; 2; 3; 4; 5; 6; 7; 8]. As an example of cellular resolution capabilities, AO-OCT based methods can reliably quantify retinal ganglion cell (RGC) soma morphology [8; 9; 10; 11] and distinguish individual retinal vessels [12; 13; 14]. OCT and AO-OCT collect and register cross-sectional scans of the retina in sequence that are combined to form 3D volumes. These volumes can be probed to observe vasculature changes in their natural depth-layered plexuses in the _en face_ plane. The anatomic relationship of these plexuses has been previously well-characterized [15]. In the parafoveal and perifoveal macula, retinal vessels separate into three distinct vascular plexuses: the superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) [15]. The SVP primarily nourishes retinal ganglion cells in the ganglion cell layer (GCL). This layer also may be connected to the radial peripapillary capillary complex (RPCP) which supplies the retinal nerve fiber layer (RNFL) in the peripapillary region. The ICP lies deeper to the SVP and supplies the dendritic synapses in the GCL as well as the cells in the inner nuclear layer (INL). Finally, the DCP is at the base of the INL and mainly supplies the bipolar and horizontal cells and their connections to the photoreceptor outer nuclear layer [16]. Glaucoma is an ocular disease that significantly affects the inner retina, specifically the RGC somas and vasculature that supplies them [17]. It is a leading cause of irreversible blindness with a projected global disease prevalence of greater than 111.8 million by 2040 [18]. Reduction of intraocular pressure (IOP) is the only current treatment for the disease, but glaucoma can worsen even with adequate IOP control and up to one-third of patients develop glaucoma with an IOP in normal range [19]. This indicates the need to understand non-IOP related factors that contribute to the disease, including retinal vascular dysfunction [17; 20]. Previous studies have shown that glaucoma, and specifically thinning of the GCL is associated with lower retinal vascular density in OCT images [21]. However, GCL thickness is an approximate surrogate for RGC density. AO-OCT has provided great capability to simultaneously measure RGC and vessel characteristics. We have previously leveraged AO-OCT to determine the association between RGC density and vessel density using a laborious and semi-automated process [22]. To further explore the relationship between RGC damage and vascular dysfunction characterized by vessel drop-out, automated quantification methods to extract these metrics from AO-OCT volumes are needed. A weakly-supervised segmentation method using a deep learning algorithm has been investigated to automatically quantify individual ganglion cell layer soma in AO-OCT volumes [9]. However, automated vessel segmentation in AO-OCT volumes, which resolve vessels down to the capillary scale, has not yet been explored. Automated retinal vessel segmentation tools for this modality will be increasingly useful and relevant for AO-OCT clinical translation. Retinal blood vessel segmentation using deep learning is an area of active research in other retinal imaging modalities, such as traditional OCT and OCT angiography (OCTA) [23; 24]. While OCTA collects multiple axial cross-sections, commonly referred to as B-scans, which can be used to form a 3D volume, typically this volume is projected back onto an _en face_ 2D plane for vessel segmentation and interpretation.