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A plethora of in vitro models have been the focus of intense research to mimic the native physiological system more accurately. Among them, organ-on-a-chip or microfluidic devices gave notable results in reconstructing reproducible three-dimensional vascularized microenvironments specific to certain organs, using various approaches. However, current strategies for quantifying the morphological variation of on-chip microvascular networks (MVNs) merely remain in the 2D domain with limited indicators, which might result in misinterpretations and the loss of biologically significant information. To this end, we introduce a novel machine learning-assisted 3D analysis pipeline that is capable of extracting major assessment parameters quantifying on-chip MVNs’ morphological variation. We utilized the MV-IMPACT (MicroVascular Injection-Molded Plastic Array 3D Culture) platform for data acquisition, a high …