UV-Based 3D Hand-Object Reconstruction with Grasp Optimization

We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coordinate map. Furthermore, we introduce inference-time optimization to fine-tune the grasp and improve interactions between the hand and the object. Our pipeline increases hand shape reconstruction accuracy and produces a vibrant hand texture. Experiments on datasets such as Ho3D, FreiHAND, and DexYCB reveal that our proposed method outperforms the state-of-the-art.Comment: BMVC 2022 Spotligh

Improving Deep Regression with Ordinal Entropy

In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression.Comment: Accepted to ICLR 2023. Project page: https://github.com/needylove/OrdinalEntrop

AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation

Accurate automatic segmentation of medical images typically requires large datasets with high-quality annotations, making it less applicable in clinical settings due to limited training data. One-shot segmentation based on learned transformations (OSSLT) has shown promise when labeled data is extremely limited, typically including unsupervised deformable registration, data augmentation with learned registration, and segmentation learned from augmented data. However, current one-shot segmentation methods are challenged by limited data diversity during augmentation, and potential label errors caused by imperfect registration. To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance. Specifically, we implement a novel dual consistency constraint to ensure anatomy-aligned registration that lessens registration errors. Furthermore, we develop an adversarial training strategy to augment the atlas image, which ensures both generation diversity and segmentation robustness. We also propose to rectify potential label errors in the augmented atlas images by estimating segmentation uncertainty, which can compensate for the imperfect nature of deformable registration and improve segmentation authenticity. Experiments on the CANDI and ABIDE datasets demonstrate that the proposed AdLER outperforms previous state-of-the-art methods by 0.7% (CANDI), 3.6% (ABIDE "seen"), and 4.9% (ABIDE "unseen") in segmentation based on Dice scores, respectively. The source code will be available at https://github.com/hsiangyuzhao/AdLER

Transmission of H7N9 influenza virus in mice by different infective routes.

BackgroundOn 19 February 2013, the first patient infected with a novel influenza A H7N9 virus from an avian source showed symptoms of sickness. More than 349 laboratory-confirmed cases and 109 deaths have been reported in mainland China since then. Laboratory-confirmed, human-to-human H7N9 virus transmission has not been documented between individuals having close contact; however, this transmission route could not be excluded for three families. To control the spread of the avian influenza H7N9 virus, we must better understand its pathogenesis, transmissibility, and transmission routes in mammals. Studies have shown that this particular virus is transmitted by aerosols among ferrets.MethodsTo study potential transmission routes in animals with direct or close contact to other animals, we investigated these factors in a murine model.ResultsViable H7N9 avian influenza virus was detected in the upper and lower respiratory tracts, intestine, and brain of model mice. The virus was transmissible between mice in close contact, with a higher concentration of virus found in pharyngeal and ocular secretions, and feces. All these biological materials were contagious for naïve mice.ConclusionsOur results suggest that the possible transmission routes for the H7N9 influenza virus were through mucosal secretions and feces

Clinical value of styrofoam fixation in intracranial tumor radiotherapy

ObjectiveTo analyze the application value of two postural fixation techniques.(styrofoam combined with head mask and fixed headrest combined with head mask) in intracranial tumor radiotherapy via cone beam computed tomography (CBCT).MethodsThis study included 104 patients with intracranial tumors undergoing radiotherapy. The patients were divided into two groups: Group A (54 cases with styrofoam fixation) and Group B (50 cases with fixed headrest fixation). The positional deviation in 3D space between the two groups was compared using CBCT. The set-up errors were expressed as median (25th percentile, 75th percentile)or M(p25, p75) since the set-up errors in all directions were not normally distributed,The Mann-Whitney U test was performed.ResultsThe age and gender of patients in the two groups were not significantly different. The set-up errors of A in lateral (X), longitudinal (Y), vertical (Z), and yaw(Rtn) axes were 1.0 (0,1) mm, 1.0 (0,1) mm, 1.0 (0,2) mm, and 0.4 (0.1, 0.8) degrees, respectively while the set-up errors of B were 1.0 (0,1) mm, 1.0 (1,2) mm, 1.0 (0,2) mm, and 0.5 (0.15,0.9) degrees, respectively. Moreover, patients in the styrofoam group had significantly smaller set-up errors in the Y-axis than patients in the headrest group (p=0.001). However, set-up errors in the X, Z, and Rtn axes were not significantly different between the two groups. The expansion boundaries of the target area in the X, Y, and Z directions were 1.77 mm, 2.45 mm, and 2.47 mm, respectively. The outer expansion boundaries of the headrest group were 2.03 mm, 3.88 mm, and 2.57 mm in X, Y, and Z directions, respectively. The set-up times of groups A and B were (32.71 ± 5.21) seconds and (46.57 ± 6.68) seconds, respectively (p=0.014). Patients in group A had significantly better comfort satisfaction than patients in group B (p=0.001).ConclusionStyrofoam plus head thermoplastic mask body fixation technique has a higher positional accuracy in intracranial tumor radiotherapy than headrest plus head thermoplastic mask fixation. Besides, styrofoam plus head thermoplastic mask body fixation technique is associated with improved positioning efficiency, and better comfort than headrest plus head thermoplastic mask fixation, and thus can be effectively applied for intracranial tumor radiotherapy positioning