Neuron Segmentation using Incomplete and Noisy Labels via Adaptive Learning with Structure Priors

Recent advances in machine learning have shown significant success in biomedical image segmentation. Most existing high-quality segmentation algorithms rely on supervised learning with full training labels. However, such methods are more susceptible to label quality; besides, generating accurate labels in biomedical data is a labor- and time-intensive task. In this paper, we propose a novel neuron segmentation method that uses only incomplete and noisy labels. The proposed method employs a noise-tolerant adaptive loss that handles partially annotated labels. Moreover, the proposed reconstruction loss leverages prior knowledge of neuronal cell structures to reduce false segmentation near noisy labels. The proposed loss function outperforms several widely used state-of-the-art noise-tolerant losses, such as reverse cross entropy, normalized cross entropy and noise-robust dice losses.