  Clinical examination of three-dimensional image data of compound anatomical
objects, such as complex joints, remains a tedious process, demanding the time
and the expertise of physicians. For instance, automation of the segmentation
task of the TMJ (temporomandibular joint) has been hindered by its compound
three-dimensional shape, multiple overlaid textures, an abundance of
surrounding irregularities in the skull, and a virtually omnidirectional range
of the jaw's motion - all of which extend the manual annotation process to more
than an hour per patient. To address the challenge, we invent a new angle to
the 3D segmentation task: namely, we propose to segment empty spaces between
all the tissues surrounding the object - the so-called negative volume
segmentation. Our approach is an end-to-end pipeline that comprises a V-Net for
bone segmentation, a 3D volume construction by inflation of the reconstructed
bone head in all directions along the normal vector to its mesh faces.
Eventually confined within the skull bones, the inflated surface occupies the
entire "negative" space in the joint, effectively providing a
geometrical/topological metric of the joint's health. We validate the idea on
the CT scans in a 50-patient dataset, annotated by experts in maxillofacial
medicine, quantitatively compare the asymmetry given the left and the right
negative volumes, and automate the entire framework for clinical adoption.
