Automated segmentation using deep-learning can expedite segmentation tasks, but algorithm generalizability to unseen datasets is unknown. Here, we used two knee segmentation algorithms, each trained separately on Osteoarthritis Initiative double-echo steady-state (DESS) scans and quantitative DESS (qDESS) scans, to segment cartilage and meniscus from qDESS datasets from four independent studies. We compared manual-vs-automatic segmentation accuracy for morphology and T2 map variations. We show that OAI-DESS-trained models may be suitable for quantifying relaxometry parameters in qDESS datasets but likely require fine-tuning to accurately quantify cartilage morphology. In contrast, qDESS-trained models generalize well to additional qDESS datasets for both morphology and T2.