Automatic Scan Planning for Magnetic Resonance Imaging of the Knee Joint

Automatic scan planning for magnetic resonance imaging of the knee aims at defining an oriented bounding box around the knee joint from sparse scout images in order to choose the optimal field of view for the diagnostic images and limit acquisition time. We propose a fast and fully automatic method to perform this task based on the standard clinical scout imaging protocol. The method is based on sequential Chamfer matching of 2D scout feature images with a three-dimensional mean model of femur and tibia. Subsequently, the joint plane separating femur and tibia, which contains both menisci, can be automatically detected using an information-augmented active shape model on the diagnostic images. This can assist the clinicians in quickly defining slices with standardized and reproducible orientation, thus increasing diagnostic accuracy and also comparability of serial examinations. The method has been evaluated on 42 knee MR images. It has the potential to be incorporated into existing systems because it does not change the current acquisition protoco

Accurate Segmentation of CT Pelvic Organs via Incremental Cascade Learning and Regression-based Deformable Models

Accurate segmentation of male pelvic organs from computed tomography (CT) images is important in image guided radiotherapy (IGRT) of prostate cancer. The efficacy of radiation treatment highly depends on the segmentation accuracy of planning and treatment CT images. Clinically manual delineation is still generally performed in most hospitals. However, it is time consuming and suffers large inter-operator variability due to the low tissue contrast of CT images. To reduce the manual efforts and improve the consistency of segmentation, it is desirable to develop an automatic method for rapid and accurate segmentation of pelvic organs from planning and treatment CT images. This dissertation marries machine learning and medical image analysis for addressing two fundamental yet challenging segmentation problems in image guided radiotherapy of prostate cancer. Planning-CT Segmentation. Deformable models are popular methods for planning-CT segmentation. However, they are well known to be sensitive to initialization and ineffective in segmenting organs with complex shapes. To address these limitations, this dissertation investigates a novel deformable model named regression-based deformable model (RDM). Instead of locally deforming the shape model, in RDM the deformation at each model point is explicitly estimated from local image appearance and used to guide deformable segmentation. As the estimated deformation can be long-distance and is spatially adaptive to each model point, RDM is insensitive to initialization and more flexible than conventional deformable models. These properties render it very suitable for CT pelvic organ segmentation, where initialization is difficult to get and organs may have complex shapes. Treatment-CT Segmentation. Most existing methods have two limitations when they are applied to treatment-CT segmentation. First, they have a limited accuracy because they overlook the availability of patient-specific data in the IGRT workflow. Second, they are time consuming and may take minutes or even longer for segmentation. To improve both accuracy and efficiency, this dissertation combines incremental learning with anatomical landmark detection for fast localization of the prostate in treatment CT images. Specifically, cascade classifiers are learned from a population to automatically detect several anatomical landmarks in the image. Based on these landmarks, the prostate is quickly localized by aligning and then fusing previous segmented prostate shapes of the same patient. To improve the performance of landmark detection, a novel learning scheme named "incremental learning with selective memory" is proposed to personalize the population-based cascade classifiers to the patient under treatment. Extensive experiments on a large dataset show that the proposed method achieves comparable accuracy to the state of the art methods while substantially reducing runtime from minutes to just 4 seconds.Doctor of Philosoph

Collaborative regression-based anatomical landmark detection

Anatomical landmark detection plays an important role in medical image analysis, e.g., for registration, segmentation and quantitative analysis. Among various existing methods for landmark detection, regression-based methods recently have drawn much attention due to robustness and efficiency. In such methods, landmarks are localized through voting from all image voxels, which is completely different from classification-based methods that use voxel-wise classification to detect landmarks. Despite robustness, the accuracy of regression-based landmark detection methods is often limited due to 1) inclusion of uninformative image voxels in the voting procedure, and 2) lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. 1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localize landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for the informative voxels near the landmark, a spherical sampling strategy is also designed in the training stage to improve their prediction accuracy. 2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of “difficult-to-detect” landmarks by using spatial guidance from “easy-to-detect” landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography (CT) images, and also dental landmarks in cone beam computed tomography (CBCT) images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods

Technical Workshop: Advanced Helicopter Cockpit Design

Information processing demands on both civilian and military aircrews have increased enormously as rotorcraft have come to be used for adverse weather, day/night, and remote area missions. Applied psychology, engineering, or operational research for future helicopter cockpit design criteria were identified. Three areas were addressed: (1) operational requirements, (2) advanced avionics, and (3) man-system integration

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion