Automatic Alignment of Pre and Intraoperative Data Using Anatomical Landmarks for Augmented Laparoscopic Liver Surgery

Image-Aligned Dynamic Liver Reconstruction Using Intra-Operative Field of Views for Minimal Invasive Surgery

During hepatic minimal invasive surgery (MIS), 3-D reconstruction of a liver surface by interpreting the geometry of its soft tissues is achieving attractions. One of the major issues to be addressed in MIS is liver deformation. Moreover, it severely inhibits free sight and dexterity of tissue manipulation, which causes its intra-operative morphology and soft tissue motion altered as compared to its pre-operative shape. While many applications focus on 3-D reconstruction of rigid or semi-rigid scenes, the techniques applied in hepatic MIS must be able to cope with a dynamic and deformable environment. We propose an efficient technique for liver surface reconstruction based on the structure from motion to handle liver deformation. The reconstructed liver will assist surgeons to visualize liver surface more efficiently with better depth perception. We use the intra-operative field of views to generate 3-D template mesh from a dense keypoint cloud. We estimate liver deformation by finding best correspondence between 3-D templates and reconstruct a liver image to calculate translation and rotational motions. Our technique then finely tunes deformed surface by adding smoothness using shading cues. Up till now, this technique is not used for solving the human liver deformation problem. Our approach is tested and validated with synthetic as well as real in vivo data, which reveal that the reconstruction accuracy can be enhanced using our approach even in challenging laparoscopic environments.

Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images

We present a method for the automatic detection of the uterus and the Fallopian tube/Uterus junctions (FU-junctions) in a monocular laparoscopic image. The main application is to perform automatic registration and fusion between preoperative radiological images of the uterus and laparoscopic images for image-guided surgery. In the broader context of computer assisted intervention, our method is the first that detects an organ and registration landmarks from laparoscopic images without manual input. Our detection problem is challenging because of the large inter-patient anatomical variability and pathologies such as uterine fibroids. We solve the problem using learned contextual geometric constraints that statistically model the positions and orientations of the FU-junctions relative to the uterus' body. We train the uterus detector using a modern part-based approach and the FU-junction detector using junction-specific context-sensitive features. We have trained and tested on a database of 95 uterus images with cross validation, and successfully detected the uterus with Recall = 0.95 and average Number of False Positives per Image (NFPI) = 0.21, and FU-junctions with Recall = 0.80 and NFPI = 0.50. Our experimental results show that the contextual constraints are fundamental to achieve high quality detection.