Hubrown8237

Experimental results demonstrate that DAIS is highly efficient to deal with a large area of missing teeth in arbitrary shapes and generate realistic occlusal surface completion. Moreover, the designed watertight inlay prostheses have enough anatomical morphology, thus providing higher clinical applicability compared with more state-of-the-art methods.Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. However, existing methods can hardly differentiate the target from its neighboring objects within the same bounding box region due to their similar textures and low-contrast boundaries. To deal with this problem, in this paper, we propose an object-guided instance segmentation method. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. To perform segmentation, an object-guided coarse-to-fine segmentation branch is built along with the detection branch. learn more The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region. To further improve the segmentation quality, we design an auxiliary feature refinement module that densely samples and refines point-wise features in the boundary regions. Experimental results on three biological image datasets demonstrate the advantages of our method. The code will be available at https//github.com/yijingru/ObjGuided-Instance-Segmentation.Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text remains challenging. In this work, we propose an end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize text of arbitrary shapes in natural scenes. PAN++ is based on the kernel representation that reformulates a text line as a text kernel (central region) surrounded by peripheral pixels. By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text. Moreover, as a pixel-based representation, the kernel representation can be predicted by a single fully convolutional network, which is very friendly to real-time applications. Taking the advantages of the kernel representation, we design a series of components as follows 1) a computationally efficient feature enhancement network composed of stacked Feature Pyramid Enhancement Modules (FPEMs); 2) a lightweight detection head cooperating with Pixel Aggregation (PA); and 3) an efficient attention-based recognition head with Masked RoI. Benefiting from the above designs, our method achieves high inference speed while maintaining competitive accuracy. Extensive experiments show the superiority of our method.Deep learning recognition approaches can potentially perform better if we can extract a discriminative representation that controllably separates nuisance factors. In this paper, we propose a novel approach to explicitly enforce the extracted discriminative representation d, extracted latent variation l (e,g., background, unlabeled nuisance attributes), and semantic variation label vector s (e.g., labeled expressions/pose) to be independent and complementary to each other. We can cast this problem as an adversarial game in the latent space of an auto-encoder. Specifically, with the to-be-disentangled s, we propose to equip an end-to-end conditional adversarial network with the ability to decompose an input sample into d and l. However, we argue that maximizing the cross-entropy loss of semantic variation prediction from d is not sufficient to remove the impact of s from d, and that the uniform-target and entropy regularization are necessary. A collaborative mutual information regularization framework is further proposed to avoid unstable adversarial training. It is able to minimize the differentiable mutual information between the variables to enforce independence. The proposed discriminative representation inherits the desired tolerance property guided by prior knowledge of the task. Our proposed framework achieves top performance on diverse recognition tasks.

While performing surgical excision for breast cancer (lumpectomy), it is important to ensure a clear margin of normal tissue around the cancer to achieve complete resection. The current standard is histopathology; however, it is time-consuming and labour-intensive requiring skilled personnel.

We describe a Hybrid Spectral-IRDx - a combination of the previously reported Spectral-IRDx tool with multimodal ultrasound and NIR spectroscopy techniques. We show how this portable, cost-effective, minimal-contact tool could provide rapid diagnosis of cancer using formalin-fixed (FF) and deparaffinized (DP) breast biopsy tissues.

Using this new tool, measurements were performed on cancerous/fibroadenoma and its adjacent normal tissues from the same patients (N=14). The acoustic attenuation coefficient () and reduced scattering coefficient (s) (at 850, 940, and 1060 nm) for the cancerous/fibroadenoma tissues were reported to be higher compared to adjacent normal tissues, a basis of delineation. Comparing FF cancerous and adjacent normal tissue, the difference in s at 850 nm and 940 nm were statistically significant (p=3.17e-2 and 7.94e-3 respectively). The difference in between the cancerous and adjacent normal tissues for DP and FF tissues were also statistically significant (p=2.85e-2 and 7.94e-3 respectively). Combining multimodal parameters and s (at 940 nm) show highest statistical significance (p=6.72e-4) between FF cancerous/fibroadenoma and adjacent normal tissues.

We show that Hybrid Spectral-IRDx can accurately delineate between cancerous and adjacent normal breast biopsy tissue.

The results obtained establish the proof-of-principle and large-scale testing of this multimodal breast cancer diagnostic platform for core biopsy diagnosis.

The results obtained establish the proof-of-principle and large-scale testing of this multimodal breast cancer diagnostic platform for core biopsy diagnosis.

The elasticity of the aortic wall varies depending on age, vessel location, and the presence of aortic diseases. Noninvasive measurement will be a powerful tool to understand the mechanical state of the aorta in a living human body. This study aimed to determine the elastic modulus of the aorta using computed tomography images.

We constructed our original formulae based on mechanics of materials. Then, we performed computed tomography scans of a silicon rubber tube by applying four pressure conditions to the lumen. The segment elastic modulus was calculated from the scanned images using our formulae. The actual modulus was measured using a tensile loading test for comparison.

The segment moduli of elasticity from the images were 0.525 [0.524, 0.527], 0.524 [0.520, 0.524], 0.520 [0.515, 0.523], and 0.522 [0.516, 0.532] (unit MPa, median [25%, 75% quantiles]) for the four pressure conditions, respectively. The corresponding measurements in the tensile test were 0.548 [0.539, 0.566], 0.535 [0.528, 0.553], 0.526 [0.513, 0.543], and 0.523 [0.508, 0.530], respectively. These results indicated errors of 4.2%, 2.1%, 1.1%, and 0.2%, respectively.

Our formulae provided good estimations of the segment elastic moduli of a silicon rubber tube under physiological pressure conditions using the computed tomography images.

In addition to the elasticity, the formulae provide the strain energy as well. These properties can be better predictors of aortic diseases. The formulae consist of clinical parameters commonly used in medical settings (pressure, diameter, and wall thickness).

In addition to the elasticity, the formulae provide the strain energy as well. These properties can be better predictors of aortic diseases. The formulae consist of clinical parameters commonly used in medical settings (pressure, diameter, and wall thickness).This study aims to validate the advantage of the new engineering method to maneuver multi-section robotic bronchoscope with first person view control in transbronchial biopsy. Six physician operators were recruited and tasked to operate a manual and a robotic bronchoscope to the peripheral area placed in patient-derived lung phantoms. The metrics collected were the furthest generation count of the airway the bronchoscope reached, force incurred to the phantoms, and NASA-Task Load Index. The furthest generation count of the airway the physicians reached using the manual and the robotic bronchoscopes were 6.6 +/- 1.2th and 6.7 +/- 0.8th. Robotic bronchoscopes successfully reached the 5th generation count into the peripheral area of the airway, while the manual bronchoscope typically failed earlier in the 3rd generation. More force was incurred to the airway when the manual bronchoscope was used (0.24 +/- 0.20 [N]) than the robotic bronchoscope was applied (0.18 +/- 0.22 [N], p less then 0.05). The manual bronchoscope imposed more physical demand than the robotic bronchoscope by NASA-TLX score (55 +/- 24 vs 19 +/- 16, p less then 0.05). These results indicate that a robotic bronchoscope facilitates the advancement of the bronchoscope to the peripheral area with less physical demand to physician operators. The metrics collected in this study would expect to be used as a benchmark for the future development of robotic bronchoscopes.Corpus callosum dysgenesis (CCD) is a congenital disorder that incorporates either partial or complete absence of the largest cerebral commissure. Remodelling of the interhemispheric fissure (IHF) provides a substrate for callosal axons to cross between hemispheres, and its failure is the main cause of complete CCD. However, it is unclear whether defects in this process could give rise to the heterogeneity of expressivity and phenotypes seen in human cases of CCD. We identify incomplete IHF remodelling as the key structural correlate for the range of callosal abnormalities in inbred and outcrossed BTBR mouse strains, as well as in humans with partial CCD. We identify an eight base-pair deletion in Draxin and misregulated astroglial and leptomeningeal proliferation as genetic and cellular factors for variable IHF remodelling and CCD in BTBR strains. These findings support a model where genetic events determine corpus callosum structure by influencing leptomeningeal-astroglial interactions at the IHF.Members of the SH3- and ankyrin repeat (SHANK) protein family are considered as master scaffolds of the postsynaptic density of glutamatergic synapses. Several missense mutations within the canonical SHANK3 isoform have been proposed as causative for the development of autism spectrum disorders (ASDs). However, there is a surprising paucity of data linking missense mutation-induced changes in protein structure and dynamics to the occurrence of ASD-related synaptic phenotypes. In this proof-of-principle study, we focus on two ASD-associated point mutations, both located within the same domain of SHANK3 and demonstrate that both mutant proteins indeed show distinct changes in secondary and tertiary structure as well as higher conformational fluctuations. Local and distal structural disturbances result in altered synaptic targeting and changes of protein turnover at synaptic sites in rat primary hippocampal neurons.