Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

Silhouette-based Pose Estimation for Deformable Organs Application to Surgical Augmented Reality

International audience— In this paper we introduce a method for semi-automatic registration of 3D deformable models using 2D shape outlines (silhouettes) extracted from a monocular camera view. Our framework is based on the combination of a biomechanical model of the organ with a set of projective constraints influencing the deformation of the model. To enforce convergence towards a global minimum for this ill-posed problem we interactively provide a rough (rigid) estimation of the pose. We show that our approach allows for the estimation of the non-rigid 3D pose while relying only on 2D information. The method is evaluated experimentally on a soft silicone gel model of a liver, as well as on real surgical data, providing augmented reality of the liver and the kidney using a monocular laparoscopic camera. Results show that the final elastic registration can be obtained in just a few seconds, thus remaining compatible with clinical constraints. We also evaluate the sensitivity of our approach according to both the initial alignment of the model and the silhouette length and shape

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Functional Liftings of Vectorial Variational Problems with Laplacian Regularization

We propose a functional lifting-based convex relaxation of variational problems with Laplacian-based second-order regularization. The approach rests on ideas from the calibration method as well as from sublabel-accurate continuous multilabeling approaches, and makes these approaches amenable for variational problems with vectorial data and higher-order regularization, as is common in image processing applications. We motivate the approach in the function space setting and prove that, in the special case of absolute Laplacian regularization, it encompasses the discretization-first sublabel-accurate continuous multilabeling approach as a special case. We present a mathematical connection between the lifted and original functional and discuss possible interpretations of minimizers in the lifted function space. Finally, we exemplarily apply the proposed approach to 2D image registration problems.Comment: 12 pages, 3 figures; accepted at the conference "Scale Space and Variational Methods" in Hofgeismar, Germany 201

Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion

Towards Real-time Remote Processing of Laparoscopic Video

Laparoscopic surgery is a minimally invasive technique where surgeons insert a small video camera into the patient\u27s body to visualize internal organs and use small tools to perform these procedures. However, the benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic system is the daVinci-si robotic surgical vision system. The video streams generate approximately 360 megabytes of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Real-time processing this large stream of data on a bedside PC, single or dual node setup, may be challenging and a high-performance computing (HPC) environment is not typically available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate (fps), it is required that each 11.9 MB (1080p) video frame be processed by a server and returned within the time this frame is displayed or 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. We have implemented and compared performance of compression, segmentation and registration algorithms on Clemson\u27s Palmetto supercomputer using dual Nvidia graphics processing units (GPUs) per node and compute unified device architecture (CUDA) programming model. We developed three separate applications that run simultaneously: video acquisition, image processing, and video display. The image processing application allows several algorithms to run simultaneously on different cluster nodes and transfer images through message passing interface (MPI). Our segmentation and registration algorithms resulted in an acceleration factor of around 2 and 8 times respectively. To achieve a higher frame rate, we also resized images and reduced the overall processing time. As a result, using high-speed network to access computing clusters with GPUs to implement these algorithms in parallel will improve surgical procedures by providing real-time medical image processing and laparoscopic data

Non-rigid registration on histopathological breast cancer images using deep learning

Cancer is one of the leading causes of death in the world, in particular, breast cancer is the most frequent in women. Early detection of this disease can significantly increase the survival rate. However, the diagnosis is difficult and time-consuming. Hence, many artificial intelligence applications have been deployed to speed up this procedure. In this MSc thesis, we propose an automatic framework that could help pathologists to improve and speed up the first step of the diagnosis of cancer. It will facilitate the cross-slide analysis of different tissue samples extracted from a selected area where cancer could be present. It will allow either pathologists to easily compare tissue structures to understand the disease's seriousness or the automatic analysis algorithms to work with several stains at once. The proposed method tries to align pairs of high-resolution histological images, curving and stretching part of the tissue by applying a deformation field to one image of the pair