Adaptive optics-optical coherence tomography (AO-OCT) enables cellular-level in vivo visualization of cone photoreceptors in the human retina. Cone biomarkers, such as density, inner segment (IS), and outer segment (OS) lengths, are potentially important for the early detection of many outer retinal conditions. However, their dense spatial packing necessitates automated analytical methods, and most existing approaches focus primarily on cone detection without addressing their detailed structural characteristics. To address this limitation, a unified neural network, termed ISOSNet, is introduced for simultaneous cone detection and IS/OS length measurement. Labeled AO-OCT B-scan datasets, encompassing healthy individuals across multiple retinal locations, were collected for model training and evaluation. Experimental results demonstrate an F1 score of 0.886 for cone detection and relative error rates of 6% and 11% for IS and OS length measurement, respectively. Validation on images from diseased retinas-despite the model being trained only on healthy retina data-highlights the generalizability of the proposed framework.