  Development of deep learning systems for biomedical segmentation often
requires access to expert-driven, manually annotated datasets. If more than a
single expert is involved in the annotation of the same images, then the
inter-expert agreement is not necessarily perfect, and no single expert
annotation can precisely capture the so-called ground truth of the regions of
interest on all images. Also, it is not trivial to generate a reference
estimate using annotations from multiple experts. Here we present a deep neural
network, defined as U-Net-and-a-half, which can simultaneously learn from
annotations performed by multiple experts on the same set of images.
U-Net-and-a-half contains a convolutional encoder to generate features from the
input images, multiple decoders that allow simultaneous learning from image
masks obtained from annotations that were independently generated by multiple
experts, and a shared low-dimensional feature space. To demonstrate the
applicability of our framework, we used two distinct datasets from digital
pathology and radiology, respectively. Specifically, we trained two separate
models using pathologist-driven annotations of glomeruli on whole slide images
of human kidney biopsies (10 patients), and radiologist-driven annotations of
lumen cross-sections of human arteriovenous fistulae obtained from
intravascular ultrasound images (10 patients), respectively. The models based
on U-Net-and-a-half exceeded the performance of the traditional U-Net models
trained on single expert annotations alone, thus expanding the scope of
multitask learning in the context of biomedical image segmentation.
