Photoacoustic (PA) imaging is a promising new imaging technology for non-invasively visualizing blood vessels inside biological tissues. In addition to blood vessels, body hairs are also visualized in PA imaging, and the body hair signals degrade the visibility of blood vessels. For learning a body hair classifier, the amount of real training and test data is limited, because PA imaging is a new modality. To address this problem, we propose a novel semi-supervised learning (SSL) method for extracting body hairs. The method effectively learns the discriminative model from small labeled training data and small unlabeled test data by introducing prior knowledge, of the orientation similarity among adjacent body hairs, into SSL. Experimental results using real PA data demonstrate that the proposed approach is effective for extracting body hairs as compared with several baseline methods.

This study aimed to characterize in vivo human digital arteries in three-dimensions using photoacoustic tomography in order to understand the specific mechanism underlying arterial deformation associated with movement of the proximal interphalangeal joint. Three-dimensional morphological data were obtained on the radialis indicis artery (radial artery of the index finger) at different angles of the joint. The association between increased curvature of the deformation and the anatomical region was assessed. Characteristic morphological deformations in areas of major deformation were determined. The deformation of the artery was characterized by three consecutive curves in juxta-articular regions, which were particularly noticeable when the joint was flexed at an angle of???60°. The change in the curvature of the deformation during 30°?90° of flexion was lower in middle-aged individuals than in young individuals. Better understanding of the mechanism underlying deformation of the digital arteries may contribute to advancements in flap procedures and rehabilitation strategies after digital artery repair.

The skin has a 3-dimensional structure that includes several appendages, such as sweat glands and sebaceous glands, as well as lymphatic and blood vessels and nerves. In spite of its complex three-dimensional structure, histological studies of the skin, including quantitative analyses of specific cell types and structures, predominantly use two-dimensional sections. Thus, for example the tortuosity of a blood vessel is not captured in histological sections. We next analyzed the pattern of cutaneous capillaries in different areas of the skin, namely the cheek, eyelid and back skin, and investigated whether there might occur changes during aging. First, the dermal area was divided into an upper and lower area and morphometric 3-dimensional analyses, including determination of the relative volume, diameter and number of branching points of capillaries were performed in an upper dermal area of 300 mm-depth below the epidermis. We found that eyelid skin has the densest capillary network below the epidermal layer, as compared to the cheek skin and the back skin

We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typical 3D imaging technologies are expensive and carrying the risk of irradiation exposure. To reduce the potential harm, we only need to take two X-ray images before visualizing the vessels. Our system can effectively reconstruct and visualize vessels in following steps. We first conduct initial segmentation using Markov Random Field and then refine segmentation in an entropy based post-process. We parse the segmented vessels by extracting their centerlines and generating trees. We propose a coarse-to-fine scheme for stereo matching, including initial matching using affine transform and dense matching using Hungarian algorithm guided by Gaussian regression. Finally, we render and visualize the reconstructed model in a HoloLens based AR system, which can essentially change the way of visualizing medical data. We have evaluated its performance by using synthetic and real stereo X-ray images, and achieved satisfactory quantitative and qualitative results.

In this paper, we focus on semi-supervised learning for biomedical image segmentation, so as to take advantage of huge unlabelled data. We observe that there usually exist some homogeneous connected areas of low confidence in biomedical images, which tend to confuse the classifier trained with limited labelled samples. To cope with this difficulty, we propose to construct forest oriented super pixels(voxels) to augment the standard random forest classifier, in which super pixels(voxels) are built upon the forest based code. Compared to the state-of-the-art, our proposed method shows superior segmentation performance on challenging 2D/3D biomedical images. The full implementation (based on Matlab) is available at https://github.com/lingucv/ssl_superpixels.

In transmitted light microscopy, a specimen tends to be observed as unclear. This is caused by a phenomenon that an image sensor captures the sum of these scattered light rays traveled from different paths due to scattering. To cope with this problem, we propose a novel computational photography approach for separating directly transmitted light from the scattering light in a transmitted light microscope by using high-frequency lighting. We first investigated light paths and clarified what types of light overlap in transmitted light microscopy. The scattered light can be simply represented and removed by using the difference in observations between focused and unfocused conditions, where the high-frequency illumination becomes homogeneous. Our method makes a novel spatial multiple-spectral absorption analysis possible, which requires absorption coefficients to be measured in each spectrum at each position. Experiments on real biological tissues demonstrated the effectiveness of our method.