  Segmenting both bone surface and the corresponding acoustic shadow are
fundamental tasks in ultrasound (US) guided orthopedic procedures. However,
these tasks are challenging due to minimal and blurred bone surface response in
US images, cross-machine discrepancy, imaging artifacts, and low
signal-to-noise ratio. Notably, bone shadows are caused by a significant
acoustic impedance mismatch between the soft tissue and bone surfaces. To
leverage this mutual information between these highly related tasks, we propose
a single end-to-end network with a shared transformer-based encoder and task
independent decoders for simultaneous bone and shadow segmentation. To share
complementary features, we propose a cross task feature transfer block which
learns to transfer meaningful features from decoder of shadow segmentation to
that of bone segmentation and vice-versa. We also introduce a correspondence
consistency loss which makes sure that network utilizes the inter-dependency
between the bone surface and its corresponding shadow to refine the
segmentation. Validation against expert annotations shows that the method
outperforms the previous state-of-the-art for both bone surface and shadow
segmentation.
