Relationship between the degree of subchondral collapse and articular surface irregularities in osteonecrosis of the femoral head

Articular surface irregularities are often observed in collapsed femoral heads with osteonecrosis, while the effects of the degree of collapse on the articular surface are poorly understood. We first macroscopically assessed the articular surface irregularities on 2-mm coronal slices obtained using high-resolution microcomputed tomography of 76 surgically resected femoral heads with osteonecrosis. These irregularities were observed in 68/76 femoral heads, mainly at the lateral boundary of the necrotic region. The mean degree of collapse was significantly larger for femoral heads with articular surface irregularities than for those without (p < 0.0001). Receiver operating characteristic analysis showed that the cutoff value for the degree of collapse in femoral heads with articular surface irregularities at the lateral boundary was 1.1 mm. Next, for femoral heads with <3-mm collapse (n = 28), articular surface irregularities were quantitatively assessed based on the number of automatically counted negative curvature points. Quantitative evaluation showed that the degree of collapse was positively correlated with the presence of articular surface irregularities (r = 0.95, p < 0.0001). Histological examination of articular cartilage above the necrotic region (n = 8) revealed cell necrosis in the calcified layer and abnormal cellular arrangement in the deep and middle layers. In conclusion, articular surface irregularities of the necrotic femoral head depended on the degree of collapse, and articular cartilage was already altered even in the absence of macroscopically determined gross irregularities.

Skin tactile surface restoration using deep learning from a mobile image: An application for virtual skincare

BACKGROUND

For virtual skincare using a touch feedback interface, reconstructing a 3D skin tactile surface from a mobile skin image is imperative for a dermatologist to palpate the skin surface that presents tactile characteristics of the subcutaneous tissues. However, the precise tactile reconstruction from a single view image is a challenging research problem due to varying illumination conditions.

METHODS

In this study, a deep learning-based tactile reconstruction scheme is proposed to restore tactile properties from light distortion and reconstruct the 3D tactile surface from a mobile skin image. Our method consists of light distortion removal using deep learning, cGAN, and 3D tactile surface generation based on image gradients.

RESULTS

The proposed method was tested by conducting two evaluation experiments in terms of removing light distortion and reconstructing 3D skin tactile surface in comparison with other well-known methods. The results demonstrated that our method outperforms existing other methods in both illumination-free image restoration and 3D surface reconstruction.

CONCLUSION

The proposed method is a promising approach in that tactile property distorted by illuminations can be completely restored using deep learning with a smaller training set and the precise reconstruction of 3D skin tactile surface can be achieved to be ready for a remotely touchable interface for virtual skincare applications.

Current and Future Perspectives on Skin Tissue Engineering: Key Features of Biomedical Research, Translational Assessment, and Clinical Application

The skin is responsible for several important physiological functions and has enormous clinical significance in wound healing. Tissue engineered substitutes may be used in patients suffering from skin injuries to support regeneration of the epidermis, dermis, or both. Skin substitutes are also gaining traction in the cosmetics and pharmaceutical industries as alternatives to animal models for product testing. Recent biomedical advances, ranging from cellular-level therapies such as mesenchymal stem cell or growth factor delivery, to large-scale biofabrication techniques including 3D printing, have enabled the implementation of unique strategies and novel biomaterials to recapitulate the biological, architectural, and functional complexity of native skin. This progress report highlights some of the latest approaches to skin regeneration and biofabrication using tissue engineering techniques. Current challenges in fabricating multilayered skin are addressed, and perspectives on efforts and strategies to meet those limitations are provided. Commercially available skin substitute technologies are also examined, and strategies to recapitulate native physiology, the role of regulatory agencies in supporting translation, as well as current clinical needs, are reviewed. By considering each of these perspectives while moving from bench to bedside, tissue engineering may be leveraged to create improved skin substitutes for both in vitro testing and clinical applications.

Accurate depth estimation of skin surface using a light-field camera toward dynamic haptic palpation

BACKGROUND

Haptic skin palpation with three-dimensional skin surface reconstruction from in vivo skin images in order to acquire both tactile and visual information has been receiving much attention. However, the depth estimation of skin surface, using a light field camera that creates multiple images with a micro-lens array, is a difficult problem due to low-resolution images resulting in erroneous disparity matching.

METHODS

Multiple low-resolution images decoded from a light field camera have limitations to accurate 3D surface reconstruction needed for haptic palpation. To overcome this, a deep learning method, Generative Adversarial Networks, was employed to generate super-resolved skin images that preserve surface detail without blurring, and then, accurate skin depth was estimated by taking multiple subsequent steps including lens distortion correction, sub-pixel shifted image generation using phase shift theorem, cost-volume building, multi-label optimization, and hole filling and refinement, which is a new approach for 3D skin surface reconstruction.

RESULTS

Experimental results of the deep-learning-based super-resolution method demonstrated that the textural detail (wrinkles) of super-resolved skin images is well preserved, unlike other super-resolution methods. In addition, the depth maps computed with our proposed algorithm verify that our method can produce more accurate and robust results compared to other state-of-the-art depth map computation methods.

CONCLUSION

Herein, we first proposed depth map estimation of skin surfaces using a light field camera and subsequently tested it with several skin images. The experimental results established the superiority of the proposed scheme.

Gradient-based 3D skin roughness rendering from an in-vivo skin image for dynamic haptic palpation

BACKGROUND/PURPOSE

Many studies have revealed the importance of palpation for dermatologists; however, palpation is not always possible due to the risk of secondary infections or the risk of damaging the affected area. Thus, haptic rendering for indirect palpation using in-vivo skin images, which will enable to examine a real three-dimensional (3D) skin sample by virtual touch without directly palpating the infected skin area, could be a useful technology in dermatology.

METHODS

We propose a new method of accurate 3D skin surface reconstruction using simple gradients from a single skin image for accurate 3D roughness rendering with a haptic device. Our approach takes advantage of bilateral filtering to preserve skin roughness and image gradients in order to generate a 3D skin surface (polygonal meshes) while preserving skin wrinkles and rough surface textures.

RESULTS

Our method was evaluated using two experiments. The accuracy was tested with six 3D ground-truth surfaces and four clinical skin images (acne, miliaria, sweet syndrome, and herpes simplex). For objective evaluation, a well-known 3D roughness estimation method and the Hausdorff distance were adopted to compute errors. All results showed that the accuracy of our method is superior to that of the two existing methods.

CONCLUSION

Haptic roughness rendering for skin palpation examination requires efficient and accurate 3D surface reconstruction. In this study, we developed a new method that can be used to reconstruct a 3D skin surface accurately while preserving roughness through the use of a single skin image.

Illumination-Insensitive Skin Depth Estimation from a Light-Field Camera Based on CGANs toward Haptic Palpation

A depth estimation has been widely studied with the emergence of a Lytro camera. However, skin depth estimation using a Lytro camera is too sensitive to the influence of illumination due to its low image quality, and thus, when three-dimensional reconstruction is attempted, there are limitations in that either the skin texture information is not properly expressed or considerable numbers of errors occur in the reconstructed shape. To address these issues, we propose a method that enhances the texture information and generates robust images unsusceptible to illumination using a deep learning method, conditional generative adversarial networks (CGANs), in order to estimate the depth of the skin surface more accurately. Because it is difficult to estimate the depth of wrinkles with very few characteristics, we have built two cost volumes using the difference of the pixel intensity and gradient, in two ways. Furthermore, we demonstrated that our method could generate a skin depth map more precisely by preserving the skin texture effectively, as well as by reducing the noise of the final depth map through the final depth-refinement step (CGAN guidance image filtering) to converge into a haptic interface that is sensitive to the small surface noise.