Segmentation of the Scaphoid Bone in Ultrasound Images

For the percutaneous fixation of scaphoid fractures, navigated approaches have been proposed to facilitate screw placement. Based on ultrasound imaging, navigation can be carried out in a cost-effective and fast manner, furthermore avoiding harmful radiation. For this purpose, a fast and efficient architecture for the automated segmentation of scaphoid bone in ultrasound volume images is needed. Methods: For 2D segmentation of the scaphoid, two architectures are taken into account: 2D nnUNet and Deeplabv3+. These architectures are trained and evaluated on a newly created dataset consisting of 67 annotated in-vivo ultrasound volume images (4576 slice images). Results: In terms of Dice coefficient, the 2D nnUNet achieves 0.67 compared to 0.57 for the Deeplabv3+. In terms of distance metrics, the 2D nnUNet shows an average symmetric surface distance error of 0.66mm, while the Deeplabv3+ achieves 0.55mm. Conclusion: Fast and accurate segmentation of the scaphoid in ultrasound volumes is feasible. Both architectures show competitive results.

Enhanced second-harmonic generation in monolayer MoS2 on suspended metallic nanostructures by plasmonic resonances

Monolayer transition metal dichalcogenides (TMDs) possess large second-order nonlinear responses due to the broken inversion symmetry, which can extend their intriguing applications in nonlinear nanophotonics and optoelectronics. However, the atomic thickness of monolayer TMDs severely decreases the interaction length with free light with respect to bulk materials, leading to rather low second-harmonic generation (SHG) conversion efficiency. Here, we demonstrate a hybrid structure consisting of a monolayer MoS2 on a suspended perforated silver film, on which the SHG signal emitted from the monolayer MoS2 is enhanced by more than three orders of magnitude at room temperature. The pronounced SHG enhancement is attributed to the distinct electric field amplification nearby the nanoholes, which is induced by the symmetric surface plasmon polaritons (SPPs) existing in the ultrathin suspended silver grating. Our results reported here may establish the substrate-free engineering of nonlinear optical effects via plasmonic nanostructures on demand.

Edge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images

Deformable image registration is of essential important for clinical diagnosis, treatment planning, and surgical navigation. However, most existing registration solutions require separate rigid alignment before deformable registration, and may not well handle the large deformation circumstances. We propose a novel edge-aware pyramidal deformable network (referred as EPReg) for unsupervised volumetric registration. Specifically, we propose to fully exploit the useful complementary information from the multi-level feature pyramids to predict multi-scale displacement fields. Such coarse-to-fine estimation facilitates the progressive refinement of the predicted registration field, which enables our network to handle large deformations between volumetric data. In addition, we integrate edge information with the original images as dual-inputs, which enhances the texture structures of image content, to impel the proposed network pay extra attention to the edge-aware information for structure alignment. The efficacy of our EPReg was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI30. Experiments demonstrate our EPReg consistently outperformed several cutting-edge methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). The proposed EPReg is a general solution for the problem of deformable volumetric registration.

CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $$0.495\pm 0.309$$ 0.495 ± 0.309 mm and Dice coefficient of $$0.985\pm 0.011$$ 0.985 ± 0.011 . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image

Volumetric liver segmentation is a prerequisite for liver transplantation and radiation therapy planning. In this paper, dilated deep residual network (DDRN) has been proposed for automatic segmentation of liver from CT images. The combination of three parallel DDRN is cascaded with fourth DDRN in order to get final result. The volumetric CT data of 40 subjects belongs to “Combined Healthy Abdominal Organ Segmentation” (CHAOS) challenge 2019 is utilized to evaluate the proposed method. Input image converted into three images using windowing ranges and fed to three DDRN. The output of three DDRN along with original image fed to the fourth DDRN as an input. The output of cascaded network is compared with the three parallel DDRN individually. Obtained results were quantitatively evaluated with various evaluation parameters. The results were submitted to online evaluation system, and achieved average dice coefficient is 0.93±0.02; average symmetric surface distance (ASSD) is 4.89±0.91. In conclusion, obtained results are prominent and consistent.