Class imbalance in image segmentation neural networks can cause overfitting to small structures, leading to inaccurate results. The distribution of logit activations may shift during testing, causing under-segmentation of these structures. To address this, new loss functions and techniques have been introduced to counteract this bias. Experiments show that these modifications significantly improve segmentation accuracy compared to traditional methods.