Three-Dimensional Characterization of Red Blood Cell Shape via Stereological Methods

Red blood cells (RBCs) exhibit a variety of morphologies that reflect their physiological or pathological state. Accurate classification of these shapes is essential for clinical diagnostics and hematological research. While most current classification methods rely on two-dimensional (2D) imaging, typically obtained through smear preparations that distort the natural three-dimensional (3D) structure of RBCs, these approaches often fail to capture diagnostically relevant 3D shape information and may lead to misclassification. In this work, we propose a novel method for 3D shape classification of RBCs based on two geometric descriptors: the spherical shape factor (F) and the bending energy (E). These descriptors are estimated directly from confocal microscopy image stacks using stereological techniques, thus avoiding the need for full 3D reconstruction. This stereological framework provides efficient, reproducible, and unbiased estimates of morphological features from sets of parallel planar sections. We demonstrate the effectiveness of this approach on a dataset that includes both standard erythrocyte SDE cell types (discocytes, stomatocytes, spherocytes, and echinocytes) and various abnormal morphologies.