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

Chanmin Park, Kanggeun Lee, Su Yeon Kim, Fatma Sema Canbakis Cecen, Seok-Kyu Kwon, Won-Ki Jeong
2021 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)  
Recent advances in machine learning have demonstrated significant success in biomedical image segmentation. Most existing high-quality segmentation algorithms rely on supervised learning with full training labels. However, segmentation is more susceptible to label quality; notably, generating accurate labels in biomedical data is a labor-and time-intensive task. Especially, structure neuronal images are hard to obtain full annotation because of the entangled shape of each structure. In this
more » ... is, a neuron structure semantic segmentation algorithm is proposed on a noise label. I assume that the label has noise and propose two new novel loss functions. Adaptive loss is applied to noise pixels in different labels with prediction in partially annotated labels. These fluorescence images may have confidence that can leverage prior knowledge when each pixel has intensity. Reconstruction loss is suggested that can be regularized of neuronal cell structures to reduce false segmentation near noisy labels. Additionally, This study is aimed to verify that our method preserves the connectivity of linear structure through a novel evaluation matrix.
doi:10.1109/isbi48211.2021.9434102 fatcat:ombrx63dobdxdjymkmd3j46olm