Magnetic resonance imaging (MRI) is a powerful tool for observing and assessing the properties of brain tissue and structures. In particular, in the context of neonatal care, MR images can be used to analyze neurodevelopmental problems that may arise in premature newborns. However, the intrinsic properties of newborn MR images, combined with the high variability of MR acquisition in a clinical setting, result in complex and heterogeneous images. Segmentation methods dedicated to the processing of clinical data are essential for obtaining relevant biomarkers. In this context, the design of quality control protocols for the associated segmentation is a cornerstone for guaranteeing the accuracy and usefulness of these inferred biomarkers. In recent work, we have proposed a new method, SegSRGAN, designed for super-resolution reconstruction and segmentation of specific brain structures. In this article, we first propose an extension of SegSRGAN from binary segmentation to multi-label segmentation, leading then to a partitioning of an MR image into several labels, each corresponding to a specific brain tissue/area. Secondly, we propose a segmentation quality control protocol designed to assess the performance of the proposed method with regard to this specific parcellation task in neonatal MR imaging. In particular, we combine scores derived from expert analysis, morphometric measurements and topological properties of the structures studied. This segmentation quality control can enable clinicians to select reliable segmentations for clinical analysis, starting with correlations between perinatal risk factors, regional volumes and specific dimensions of cognitive development. Based on this protocol, we are investigating the strengths and weaknesses of SegSRGAN and its potential suitability for clinical research in the context of morphometric analysis of brain structure in preterm infants, and to potentially design new biomarkers of neurodevelopment. The proposed study focuses on MR images from the EPIRMEX dataset, collected as part of a national cohort study. In particular, this work represents a first step towards the design of 3-dimensional neonatal brain morphometry based on segmentation.