  Objective: Medical image datasets with pixel-level labels tend to have a
limited number of organ or tissue label classes annotated, even when the images
have wide anatomical coverage. With supervised learning, multiple classifiers
are usually needed given these partially annotated datasets. In this work, we
propose a set of strategies to train one single classifier in segmenting all
label classes that are heterogeneously annotated across multiple datasets
without moving into semi-supervised learning. Methods: Masks were first created
from each label image through a process we termed presence masking. Three
presence masking modes were evaluated, differing mainly in weightage assigned
to the annotated and unannotated classes. These masks were then applied to the
loss function during training to remove the influence of unannotated classes.
Results: Evaluation against publicly available CT datasets shows that presence
masking is a viable method for training class-generic classifiers. Our
class-generic classifier can perform as well as multiple class-specific
classifiers combined, while the training duration is similar to that required
for one class-specific classifier. Furthermore, the class-generic classifier
can outperform the class-specific classifiers when trained on smaller datasets.
Finally, consistent results are observed from evaluations against human thigh
and calf MRI datasets collected in-house. Conclusion: The evaluation outcomes
show that presence masking is capable of significantly improving both training
and inference efficiency across imaging modalities and anatomical regions.
Improved performance may even be observed on small datasets. Significance:
Presence masking strategies can reduce the computational resources and costs
involved in manual medical image annotations. All codes are publicly available
at https://github.com/wong-ck/DeepSegment.
