  Accurate and automated tumor segmentation is highly desired since it has the
great potential to increase the efficiency and reproducibility of computing
more complete tumor measurements and imaging biomarkers, comparing to (often
partial) human measurements. This is probably the only viable means to enable
the large-scale clinical oncology patient studies that utilize medical imaging.
Deep learning approaches have shown robust segmentation performances for
certain types of tumors, e.g., brain tumors in MRI imaging, when a training
dataset with plenty of pixel-level fully-annotated tumor images is available.
However, more than often, we are facing the challenge that only (very) limited
annotations are feasible to acquire, especially for hard tumors. Pancreatic
ductal adenocarcinoma (PDAC) segmentation is one of the most challenging tumor
segmentation tasks, yet critically important for clinical needs. Previous work
on PDAC segmentation is limited to the moderate amounts of annotated patient
images (n<300) from venous or venous+arterial phase CT scans. Based on a new
self-learning framework, we propose to train the PDAC segmentation model using
a much larger quantity of patients (n~=1,000), with a mix of annotated and
un-annotated venous or multi-phase CT images. Pseudo annotations are generated
by combining two teacher models with different PDAC segmentation specialties on
unannotated images, and can be further refined by a teaching assistant model
that identifies associated vessels around the pancreas. A student model is
trained on both manual and pseudo annotated multi-phase images. Experiment
results show that our proposed method provides an absolute improvement of 6.3%
Dice score over the strong baseline of nnUNet trained on annotated images,
achieving the performance (Dice = 0.71) similar to the inter-observer
variability between radiologists.
