Peripheral artery disease (PAD) often requires revascularization guided by computed tomography angiography (CTA). Manual analysis of lower-limb arteries on CTA is time-consuming, operator-dependent, and limited by challenging imaging conditions. While deep learning has advanced vascular segmentation, current models focus on large central vessels and do not capture the full arterial anatomy and pathological conditions in the lower extremities for PAD. In this work, we present a fully automated 3D segmentation pipeline tailored to PAD to delineate the entire lower-limb arterial tree, including the recognition of each main and peripheral branches, along with calcified plaques and stents. Our framework leverages nnU-Net, and addresses the limitations in complex PAD settings by introducing (1) a PAD-specific annotation protocol using a semi-automated tool to reduce inter-observer variability in labeling stents and calcifications, (2) a novel object-level detection metric that accounts for boundary ambiguity in calcified lesions and stents, and (3) the incorporation of the clDice loss to enhance topological preservation, critical for vascular analysis in a pathological setting. We train and evaluate our method on a curated in-house dataset under challenging imaging conditions. Our pipeline demonstrates robust performance, enabling the extraction of clinically relevant features necessary to the pre-surgical planning. It paves the way for fully automated PAD assessment, with potential to improve diagnostic accuracy, reduce time to treatment, and more objective severity scoring for therapy planning.