Enhancing endoscopic scene reconstruction with color-aware inverse rendering through neural SDF and radiance fields.

Virtual surgical training is crucial for enhancing minimally invasive surgical skills. Traditional geometric reconstruction methods based on medical CT/MRI images often fall short in providing color information, which is typically generated through pseudo-coloring or artistic rendering. To simultaneously reconstruct both the geometric shape and appearance information of organs, we propose a novel organ model reconstruction network called Endoscope-NeSRF.

Neural radiance fields-based multi-view endoscopic scene reconstruction for surgical simulation.

Purpose: In virtual surgery, the appearance of 3D models constructed from CT images lacks realism, leading to potential misunderstandings among residents. Therefore, it is crucial to reconstruct realistic endoscopic scene using multi-view images captured by an endoscope.

Methods: We propose an Endoscope-NeRF network for implicit radiance fields reconstruction of endoscopic scene under non-fixed light source, and synthesize novel views using volume rendering.

Pulmonary nodule detection on lung parenchyma images using hyber-deep algorithm.

The incidence of lung cancer has seen a significant increase in recent times, leading to a rise in fatalities. The detection of pulmonary nodules from CT images has emerged as an effective method to aid in the diagnosis of lung cancer. Ensuring information security holds utmost significance in the detection of nodules, with particular attention given to safeguarding patient privacy within the context of the Internet of Things (IoT).

Towards Virtual VATS, Face, and Construct Evaluation for Peg Transfer Training of Box, VR, AR, and MR Trainer.

The aim of this study is to develop and assess the peg transfer training module face, content and construct validation use of the box, virtual reality (VR), cognitive virtual reality (CVR), augmented reality (AR), and mixed reality (MR) trainer, thereby to compare advantages and disadvantages of these simulators. Training system (VatsSim-XR) design includes customized haptic-enabled thoracoscopic instruments, virtual reality helmet set, endoscope kit with navigation, and the patient-specific corresponding training environment. A cohort of 32 trainees comprising 24 novices and 8 experts underwent the real and virtual simulators that were conducted in the department of thoracic surgery of Yunnan First People's Hospital.