  Morphological atlases are an important tool in organismal studies, and modern
high-throughput Computed Tomography (CT) facilities can produce hundreds of
full-body high-resolution volumetric images of organisms. However, creating an
atlas from these volumes requires accurate organ segmentation. In the last
decade, machine learning approaches have achieved incredible results in image
segmentation tasks, but they require large amounts of annotated data for
training. In this paper, we propose a self-training framework for multi-organ
segmentation in tomographic images of Medaka fish. We utilize the
pseudo-labeled data from a pretrained Teacher model and adopt a Quality
Classifier to refine the pseudo-labeled data. Then, we introduce a pixel-wise
knowledge distillation method to prevent overfitting to the pseudo-labeled data
and improve the segmentation performance. The experimental results demonstrate
that our method improves mean Intersection over Union (IoU) by 5.9% on the full
dataset and enables keeping the quality while using three times less markup.
