Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56 ± 0.39 mm and 0.87 ± 0.42 mm. We can analyze the relationship between tissue information and MR signals using the proposed method.

Laparoscopic surgery allows reduction in surgical incision size and leads to faster recovery compared with open surgery. When bleeding takes place, hemostasis treatment is planned according to the state and location of the bleeding. However, it is difficult to find the bleeding source due to low visibility caused by the narrow field of view of the laparoscope. In this paper, we propose the concept of a hemostasis support system that automatically identifies blood regions and indicates them to the surgeon. We mainly describe a blood region identification method that is one of technical challenges to realize the support system. The proposed method is based on a machine learning technique called the support vector machine, working in real time. Within this method, all the pixels in the image are classified as either blood or non-blood pixels based on color features (e.g., a combination of RGB and HSV values). The suitable combination of feature values used for the classification is determined by a simple feature selection method. Three feature values were determined to identify the blood region. We then validated the proposed method with ten sequences of laparoscopic images by cross-validation. The average accuracy exceeded 95% with a processing time of about 12.6 ms/frame. The proposed method was able to accurately identify blood regions and was suitable for real-time applications.

In diagnosis and treatment of knee joint diseases, it is effective to study the three-dimensional (3D) motion of the patient's knee joint involving the femur, tibia, and patella. A 2D/3D registration method with use of fluoroscopy and CT images is promising for this purpose. However, there is no report showing whether the dynamic 3D motion of the patella can be obtained. In this study, we tried to examine dynamic 3D motion of the knee joint which included the patella. First, in order to investigate the accuracy of the position estimation, we conducted an experiment on a pig knee joint which had several fiducial markers placed on it, and we found that errors in the estimation of rotation and translation were less than 1 mm and 1 deg. We then carried out an image-acquisition experiment with healthy knee joints of three volunteers and confirmed that 3D motions of the femur, tibia, and patella were successfully obtained for all cases.

BackgroundThe aims of this study were to reveal the characteristics of the meniscal shape at each knee osteoarthritis (OA) severity level and to predict trends or patterns of the meniscal shape change as associated with knee OA progression.MethodsFifty-one patients diagnosed with knee OA based on X-ray and magnetic resonance (MR) images were evaluated. They were divided into three groups based on the Kellgren–Lawrence (KL) grade: normal group (KL grade of 0 or 1), mild group (KL grade of 2 or 3), and severe group (KL grade of 4). We measured the patients’ meniscal size and meniscal extrusion using MR images. In addition, semiquantitative measurement was performed using MR images to determine the arthritic status of the corresponding compartment using a whole-organ magnetic resonance imaging score (WORMS).ResultsThe longitudinal diameter and posterior wedge angle of the medial meniscus were significantly larger, and the posterior wedge width of the medial meniscus was significantly smaller in the severe group than in the normal group. The WORMS scores for cartilage and osteophytes in the medial region were significantly different among the groups. The WORMS score of each region was strongly correlated with the longitudinal diameter. The WORMS scores of the lateral region were lower than those of the medial region.ConclusionOur observation of the shape change of the medial meniscus in the posterior region was roughly consistent with that in many previous studies of meniscal degeneration. On the other hand, we saw that the most relevant relation between the progression of the knee OA and the deformation of the meniscus was in the longitudinal direction.

When surgeons evaluate the condition of organs and make diagnoses, color difference is important information despite its subtleness. Yielding clearer views of blood circulation holds the key to successful surgeries such as transplants and anastomosis. Optimization of surgical illuminant is one approach to clearer views. Our previous study focused on computer simulation to enhance color difference. In the present study, we improved the simulation method by applying a color appearance model CIECAM02 and we realized an optimized illuminant based on the simulation. In an evaluation experiment comparing the optimal illuminant with the conventional illuminant, fourteen LEDs fixed to the light unit were spectrally adjusted to demonstrate the two illuminants. Using a rat cecum, we observed the color differences under two conditions: normal blood flow and restricted blood flow. The color difference under the optimal illuminant was greater than under the conventional illuminant and the effectiveness of the optimal illuminant was confirmed.