Cancer Spheroid Segmentation Based on Vision Transformer

Two-dimensional (2D) cultures have long been the standard in vitro drug discovery paradigm. However, three-dimensional (3D) culture models, e.g. spheroids and organoids, has revolutionized the screening for new bioactive substances, drug repositioning and precision medicine, once they better mimic the natural cell environment, and consequently, provide more accurate information. Despite the advances, analyzing these images of 3D structures takes a lot of time, money, and effort. Therefore, better, more precise and more adaptive segmentation models are required due to the demand for analysis automation. In this work, by contrasting its performance with CNNs that concentrate on medical image segmentation, we explore the applicability of Transformer models to the human cancer spheroid task. Additionally, we release our own dataset that is prepared for semantic segmentation benchmarking. Our experiments showed that the CNN models were superior at handling training with fewer samples because the Transformer’s self-attention mechanism requires considerably more data and training time before converging to a learned pattern.