  Precise segmentation of organs and tumors plays a crucial role in clinical
applications. It is a challenging task due to the irregular shapes and various
sizes of organs and tumors as well as the significant class imbalance between
the anatomy of interest (AOI) and the background region. In addition, in most
situation tumors and normal organs often overlap in medical images, but current
approaches fail to delineate both tumors and organs accurately. To tackle such
challenges, we propose a position-guided deformable UNet, namely PGD-UNet,
which exploits the spatial deformation capabilities of deformable convolution
to deal with the geometric transformation of both organs and tumors. Position
information is explicitly encoded into the network to enhance the capabilities
of deformation. Meanwhile, we introduce a new pooling module to preserve
position information lost in conventional max-pooling operation. Besides, due
to unclear boundaries between different structures as well as the subjectivity
of annotations, labels are not necessarily accurate for medical image
segmentation tasks. It may cause the overfitting of the trained network due to
label noise. To address this issue, we formulate a novel loss function to
suppress the influence of potential label noise on the training process. Our
method was evaluated on two challenging segmentation tasks and achieved very
promising segmentation accuracy in both tasks.
