Multiorgan segmentation from partially labeled datasets with conditional nnU-Net

Abstract

Accurate and robust multiorgan abdominal CT segmentation plays a significant role in numerous clinical applications, such as therapy treatment planning and treatment delivery. Almost all existing segmentation networks rely on fully annotated data with strong supervision. However, annotating fully annotated multiorgan data in CT images is both laborious and time-consuming. In comparison, massive partially labeled datasets are usually easily accessible. In this paper, we propose conditional nnU-Net trained on the union of partially labeled datasets for multiorgan segmentation. The deep model employs the state-of-the-art nnU-Net as the backbone and introduces a conditioning strategy by feeding auxiliary information into the decoder architecture as an additional input layer. This model leverages the prior conditional information to identify the organ class at the pixel-wise level and encourages organs' spatial information recovery. Furthermore, we adopt a deep supervision mechanism to refine the outputs at different scales and apply the combination of Dice loss and Focal loss to optimize the training model. Our proposed method is evaluated on seven publicly available datasets of the liver, pancreas, spleen and kidney, in which promising segmentation performance has been achieved. The proposed conditional nnU-Net breaks down the barriers between nonoverlapping labeled datasets and further alleviates the problem of data hunger in multiorgan segmentation.

Keywords: Conditioning strategy; Deep learning; Multiorgan segmentation; Partially labeled dataset.