Endocrine-disrupting chemicals (EDCs) are pollutants that interfere with hormone-receptor binding. In particular, agricultural pesticides have been linked to prostate cancer. To study their effects and the potential treatments, biologists use 3D in vitro organoids, captured by confocal microscopy with nuclear biomarker. Cell segmentation models are employed by biologists to produce a map that identifies the objects inside the organoid. The label masks simplify experts’ analysis. Although effective, the current models are computationally costly. Many laboratories can not afford high-performance GPUs. Moreover, labeled datasets are scarce and manual annotation is time-consuming. This thesis aims to achieve accurate cell 3D segmentation with a compact neural network on CPU-only hardware.