Vol.:(0123456789) 1 3 World Journal of Urology https://doi.org/10.1007/s00345-021-03745-y TOPIC PAPER Patient‑specifc, touch‑based registration during robotic, image‑guided partial nephrectomy Naren Nimmagadda 1  · James M. Ferguson 2  · Nicholas L. Kavoussi 1  · Bryn Pitt 2  · Eric J. Barth 2  · Josephine Granna 2  · Robert J. Webster III 2  · S. Duke Herrell III 1 Received: 23 March 2021 / Accepted: 25 May 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Image-guidance during partial nephrectomy enables navigation within the operative feld alongside a 3-dimensional roadmap of renal anatomy generated from patient-specifc imaging. Once a process is performed by the human mind, the technol- ogy will allow standardization of the task for the beneft of all patients undergoing robot-assisted partial nephrectomy. Any surgeon will be able to visualize the kidney and key subsurface landmarks in real-time within a 3-dimensional simulation, with the goals of improving operative efciency, decreasing surgical complications, and improving oncologic outcomes. For similar purposes, image-guidance has already been adopted as a standard of care in other surgical felds; we are now at the brink of this in urology. This review summarizes touch-based approaches to image-guidance during partial nephrec- tomy, as the technology begins to enter in vivo human evaluation. The processes of segmentation, localization, registration, and re-registration are all described with seamless integration into the da Vinci surgical system; this will facilitate clinical adoption sooner. Keywords Partial nephrectomy · Robot-assisted · Image-guidance · Registration · Re-registration · Touch-based Introduction The idea of image-guidance during partial nephrectomy (IGPN) arose as the field of urology adopted complex, nephron-sparing surgery as the standard of care, while simultaneously shifting towards laparoscopy and robotic assistance. Minimally invasive surgery has many advan- tages; however, the tactile feedback used to identify pul- sating arteries and tissue planes in open surgery remains limited. All the while, the challenge of distinguishing nor- mal from pathologic tissue persists, in all forms of surgery. Circumventing these limitations in the current paradigm of partial nephrectomy relies heavily on a surgeon’s men- tal coregistration—the brain’s ability to (1) reconstruct the 2-dimensional spatial relationships of anatomic structures from pre-operative imaging (i.e. renal artery to renal vein, renal tumor from normal parenchyma, etc.) into a 3-dimen- sional mental model, and then (2) align that mental model to what is visualized intra-operatively. Today, computer algorithms can fully automate this process to enable real- time navigation around observed structures and, most impor- tantly, the unseen subsurface structures beneath them. IGPN can range in complexity from manually-aligned 3-dimensional renal imaging displayed next to the endo- scope monitor to intra-operative CT used to align fducials implanted in the kidney to endoscope video. The benefts of these approaches have now been shown in several human studies [1–3]. However, the registration methods employed still have several drawbacks and/or limitations to wide- spread adoption. Most importantly, no group has achieved fully automated registration in vivo during robot-assisted partial nephrectomy that is non-invasive, fts within the current surgical workfow, and where registration accuracy has been quantitatively accessed. Touch-based registration as reviewed here has the potential to address all of these challenges. By leveraging the da Vinci (Intuitive Surgical, Sunnyvale, CA, USA) robot’s inherent knowledge of its * S. Duke Herrell III duke.herrell@vumc.org 1 Department of Urology, Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Medical Center, Nashville, TN, USA 2 Department of Mechanical Engineering, Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN, USA