  As computer vision algorithms increase in capability, their applications in
clinical systems will become more pervasive. These applications include:
diagnostics, such as colonoscopy and bronchoscopy; guiding biopsies, minimally
invasive interventions, and surgery; automating instrument motion; and
providing image guidance using pre-operative scans. Many of these applications
depend on the specific visual nature of medical scenes and require designing
algorithms to perform in this environment.
  In this review, we provide an update to the field of camera-based tracking
and scene mapping in surgery and diagnostics in medical computer vision. We
begin with describing our review process, which results in a final list of 515
papers that we cover. We then give a high-level summary of the state of the art
and provide relevant background for those who need tracking and mapping for
their clinical applications. After which, we review datasets provided in the
field and the clinical needs that motivate their design. Then, we delve into
the algorithmic side, and summarize recent developments. This summary should be
especially useful for algorithm designers and to those looking to understand
the capability of off-the-shelf methods. We maintain focus on algorithms for
deformable environments while also reviewing the essential building blocks in
rigid tracking and mapping since there is a large amount of crossover in
methods. With the field summarized, we discuss the current state of the
tracking and mapping methods along with needs for future algorithms, needs for
quantification, and the viability of clinical applications. We then provide
some research directions and questions. We conclude that new methods need to be
designed or combined to support clinical applications in deformable
environments, and more focus needs to be put into collecting datasets for
training and evaluation.
