  In surgical oncology, it is challenging for surgeons to identify lymph nodes
and completely resect cancer even with pre-operative imaging systems like PET
and CT, because of the lack of reliable intraoperative visualization tools.
Endoscopic radio-guided cancer detection and resection has recently been
evaluated whereby a novel tethered laparoscopic gamma detector is used to
localize a preoperatively injected radiotracer. This can both enhance the
endoscopic imaging and complement preoperative nuclear imaging data. However,
gamma activity visualization is challenging to present to the operator because
the probe is non-imaging and it does not visibly indicate the activity
origination on the tissue surface. Initial failed attempts used segmentation or
geometric methods, but led to the discovery that it could be resolved by
leveraging high-dimensional image features and probe position information. To
demonstrate the effectiveness of this solution, we designed and implemented a
simple regression network that successfully addressed the problem. To further
validate the proposed solution, we acquired and publicly released two datasets
captured using a custom-designed, portable stereo laparoscope system. Through
intensive experimentation, we demonstrated that our method can successfully and
effectively detect the sensing area, establishing a new performance benchmark.
Code and data are available at
https://github.com/br0202/Sensing_area_detection.git
