The endometrium undergoes rapid cycles of menstrual breakdown and repair. In each cycle, oestrogen-dependent proliferation followed by progesterone-dependent differentiation of the endometrium culminates in a sterile inflammatory tissue response, termed the decidual reaction, at the start of the embryo implantation window. Analysis of timed endometrial biopsies is widely used to investigate a spectrum of reproductive disorders, including recurrent implantation failure in IVF and recurrent miscarriage. Deep profiling of whole slide images (WSIs), capturing the spatial and functional organization of key histological structures, such as nuclei, glandular and luminal epithelium, subluminal stroma and spiral arterioles, holds significant promise for automated endometrial assessment. To address the lack of such methodologies, we developed ASTER, a multi-task deep learning model for simultaneous segmentation of multiple histological structures in immunostained endometrial WSIs. ASTER has been developed and validated over a large dataset of 2,652 endometrial whole slide images, including 35,135 annotated objects obtained using a pathologist-in-the-loop methodology. The model demonstrates strong performance across all segmentation tasks. Further, analysis of an independent set of 2,082 unseen WSIs showed that ASTER-derived features correlate with cycle-dependent endometrial gene expression. This represents the first systematic study linking segmented morphological characteristics to molecular and temporal markers of endometrial function, demonstrating its effectiveness in enabling comprehensive and automated profiling. This highlights the potential of ASTER to support personalized management of women experiencing reproductive failure. Segmentation results are available for interactive exploration at https://tiademos.dcs.warwick.ac.uk/bokeh_app?demo=ASTER .