3D Reconstruction of Laparoscope Images With Contrastive Learning Methods

The use of laparoscopic images and videos to reconstruct abdominal tissue structure overcomes the visual limitations of human eyes and provides great convenience for the detection and diagnosis of medical diseases. The method described in this paper is based on contrast learning and ORB-SLAM design. The contributions of this study are as follows. (i) A data preprocessing thread is introduced, which includes data augmentation and input frame evaluation. (ii) Encoding global information to avoid semantic information loss and eliminate mismatch by designing an improved U-NET network. (iii) The improved U-NET network was used to introduce a dual-branched Siamese network and GPU-accelerated intensive reconstruction thread. The dual-branched Siamese network structure was used to compute the depth information of the feature points and optical flow information in parallel, and the dense depth estimation was obtained without interrupting the original sparse reconstruction. (iv) An experimental system was established to conduct three-dimensional reconstruction tests on laparoscopic/endoscope /UBE videos of 186 patients. To effectively provide more accurate feature detection and matching support for the application of combat scenes, the influence of lens distortion was considered. Compared with the current mainstream three-dimension reconstruction and deep learning algorithms, the practicability and superiority of laparoscopic three-dimension reconstruction based on contrastive learning are demonstrated in clinical scenarios such as surgical navigation, auxiliary diagnosis, and surgical simulation.