Automated imaging systems are becoming important tools for medicine and biology research as they facilitate rapid analyses with better reproducibility. Segmenting regions of interest on a medical image is typically the first but one of the foremost steps of these systems, which greatly affects the success of the entire analysis. In this talk, I will briefly mention the main challenges associated with segmentation tasks in medical image analysis, and then present examples of the dense prediction networks that my research group designed and implemented to address these challenges. Particularly, I will talk about our proposed network architectures and loss functions that were specifically designed to facilitate better training of the segmentation networks. At the end, I will discuss future research possibilities towards the direction of developing more robust segmentation networks for medical image analysis.