Single 3D cell segmentation from optical CT microscope images

Abstract

The automated segmentation of the nucleus and cytoplasm regions in 3D optical CT microscope images has been achieved with two methods, a global threshold gradient based approach and a graph-cut approach. For the first method, the first two peaks of a gradient figure of merit curve are selected as the thresholds for cytoplasm and nucleus segmentation. The second method applies a graph-cut segmentation twice: the first identifies the nucleus region and the second identifies the cytoplasm region. Image segmentation of single cells is important for automated disease diagnostic systems. The segmentation methods were evaluated with 200 3D images consisting of 40 samples of 5 different cell types. The cell types consisted of columnar, macrophage, metaplastic and squamous human cells and cultured A549 cancer cells. The segmented cells were compared with both 2D and 3D reference images and the quality of segmentation was determined by the Dice Similarity Coefficient (DSC). In general, the graph-cut method had a superior performance to the gradient-based method. The graph-cut method achieved an average DSC of 86% and 72% for nucleus and cytoplasm segmentations respectively for the 2D reference images and 83% and 75% for the 3D reference images. The gradient method achieved an average DSC of 72% and 51% for nucleus and cytoplasm segmentation for the 2D reference images and 71% and 51% for the 3D reference images. The DSC of cytoplasm segmentation was significantly lower than for the nucleus since the cytoplasm was not differentiated as well by image intensity from the background.