[Retrospective study on the modified Uhl technique of closed reduction and percutaneous pin in the treatment of Colles' fracture].

OBJECTIVE: To retrospectively assess the advantages of the modified Uhl technique in the treatment of Colles' fracture guided by the principles of Chinese osteosynthesis (CO) concept. METHODS: A retrospective study was conducted on 358 patients with Colles' fracture treated with the modified Uhl technique of closed reduction and percutaneous pin between January 2016 and June 2021. Out of these, 120 eligible cases were selected and categorized into two groups according to different surgical methods:the closed reduction and percutaneous pin group, and the open reduction group. Sixty-eight patients in the closed reduction and percutaneous pin group were treated with the modified Uhl technique, while fifty-two patients in the open reduction group were treated with open reduction and internal fixation using plates. The modified Sarmiento imaging score, Gartland-Werley wrist score, operation time, hospital stay, and treatment costs between the two groups were compared at a 6-month postoperative follow-up. RESULTS: There were no significant differences in terms of gender, age, affected side, injure factors, time of injury to surgery, Sarmiento imaging score, and Gartland-Werley wrist joint score (P>0.05). The closed reduction and percutaneous pin group exhibited an operation time of (35.88±14.11) minutes, hospitalization stay of (9.78±2.48) days, and treatment costs of (16 074.91±1 964.48) yuan, while the open reduction group demonstrated comparatively longer operation time of (65.48±14.26) minutes, hospitalization stay of (15.88±2.00) days, and treatment costs of (20 451.27±1 760.22) yuan (P<0.01). CONCLUSION: The modified Uhl technique presents notable advantages in the management of Colles' fracture, including reliable fixation, less trauma, shorter operation time, less pain, shorter hospital stay, and cost-effectiveness. This technique exhibits promising potential for broader clinical application. However, it is important to note that the pin could potentially damage tendons, and in cases of Colles' fractures with osteoporosis and comminuted fragments, additional techniques may be required for reliable fixation.

Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval.

In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to learn two deep nonlinear transformations for visual feature extraction from the co-embedding feature space. Second, we propose the global loss function for the DCJN, consisting of a discriminative loss and a correlation loss. The discriminative loss aims to minimize the intraclass distance of the extracted features and maximize the interclass distance of such features to a large margin within each modality, while the correlation loss focuses on mitigating the distribution discrepancy across different modalities. Consequently, the proposed method can realize cross-modality feature extraction guided by the defined global loss function to benefit the similarity measure between 2-D images and 3-D models. For a comparison experiment, we contribute the current largest 2-D image-based 3-D model retrieval dataset. Moreover, the proposed method was further evaluated on three popular benchmarks, including the 3-D Shape Retrieval Contest 2014, 2016, and 2018 benchmarks. The extensive comparison experimental results demonstrate the superiority of this method over the state-of-the-art methods.

Monocular Image-Based 3-D Model Retrieval: A Benchmark.

Monocular image-based 3-D model retrieval aims to search for relevant 3-D models from a dataset given one RGB image captured in the real world, which can significantly benefit several applications, such as self-service checkout, online shopping, etc. To help advance this promising yet challenging research topic, we built a novel dataset and organized the first international contest for monocular image-based 3-D model retrieval. Moreover, we conduct a thorough analysis of the state-of-the-art methods. Existing methods can be classified into supervised and unsupervised methods. The supervised methods can be analyzed based on several important aspects, such as the strategies of domain adaptation, view fusion, loss function, and similarity measure. The unsupervised methods focus on solving this problem with unlabeled data and domain adaptation. Seven popular metrics are employed to evaluate the performance, and accordingly, we provide a thorough analysis and guidance for future work. To the best of our knowledge, this is the first benchmark for monocular image-based 3-D model retrieval, which aims to help related research in multiview feature learning, domain adaptation, and information retrieval.

Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.

The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided deep neural network (SSG-DNN) to jointly identify and localize mitotic events in time-lapse phase contrast microscopy images. It consists of three key modules. First, the module of visual context learning extracts static visual feature and dynamic visual transition within individual volumetric cell regions. Secondly, with these information, the module of sequential saliency modeling aims to discover the saliency distribution over all successive frames in each volumetric region. Finally, the module of sequence structure modeling can leverage both visual context and saliency distribution for mitosis identification and localization. SSG-DNN can jointly realize visual feature learning and sequential structure modeling in the end-to-end framework. Moreover, the proposed method is independent of complicated preconditioning methods for mitotic candidate extraction and can be applied for mitosis detection in one-shot manner. To our knowledge, it is the first weakly supervised work to realize joint mitosis identification and localization only with sequence-wise labels. In our experiments, we evaluate its performances of both tasks on the popular C3H10 dataset and a novel and large-scale dataset, C2C12-16, which contains much more mitotic events and is more challenging owing to diverse cell culture conditions. Experimental results can demonstrate the superiority of the proposed method.

Multi-Domain & Multi-Task Learning for Human Action Recognition.

Domain-invariant (view-invariant & modalityinvariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain & multi-task learning (MDMTL) method to (1) extract domain-invariant information for multi-view and multi-modal action representation and (2) explore the relatedness among multiple action categories. Specifically, we present a sparse transfer learning-based method to co-embed multi-domain (multi-view & multi-modality) data into a single common space for discriminative feature learning. Additionally, visual feature learning is incorporated into the multitask learning framework, with the Frobenius-norm regularization term and the sparse constraint term, for joint task modeling and task relatedness-induced feature learning. To the best of our knowledge, MDMTL is the first supervised framework to jointly realize domain-invariant feature learning and task modeling for multi-domain action recognition. Experiments conducted on the INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset, the MSR Daily Activity 3D (DailyActivity3D) dataset, and the Multi-modal & Multi-view & Interactive (M2I) dataset, which is the most recent and largest multi-view and multi-model action recognition dataset, demonstrate the superiority of MDMTL over the state-of-the-art approaches.

View-Based 3-D Model Retrieval: A Benchmark.

View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform. By quantitatively analyzing the performances, we discover the graph matching-based method with deep features, especially the clique graph matching algorithm with convolutional neural networks features, can usually outperform the others. We further discuss the future research directions in this field.

Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition.

This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery. First, we convert this non-convex objective function into the convex formulation by fixing the latent grouping information. This new objective function focuses on multi-task learning by strengthening the shared-action relationship and action-specific feature learning. Second, we leverage the learned model parameters for the task relatedness measure and clustering. In this way, HC-MTL can attain both optimal action models and group discovery by alternating iteratively. The proposed method is validated on three kinds of challenging datasets, including six realistic action datasets (Hollywood2, YouTube, UCF Sports, UCF50, HMDB51 & UCF101), two constrained datasets (KTH & TJU), and two multi-view datasets (MV-TJU & IXMAS). The extensive experimental results show that: 1) HC-MTL can produce competing performances to the state of the arts for action recognition and grouping; 2) HC-MTL can overcome the difficulty in heuristic action grouping simply based on human knowledge; 3) HC-MTL can avoid the possible inconsistency between the subjective action grouping depending on human knowledge and objective action grouping based on the feature subspace distributions of multiple actions. Comparison with the popular clustered multi-task learning further reveals that the discovered latent relatedness by HC-MTL aids inducing the group-wise multi-task learning and boosts the performance. To the best of our knowledge, ours is the first work that breaks the assumption that all actions are either independent for individual learning or correlated for joint modeling and proposes HC-MTL for automated, joint action grouping and modeling.

Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval.

Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique/edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D model retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information.

Jointly Learning Multiple Sequential Dynamics for Human Action Recognition.

Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces. For model learning, we propose the multi-task conditional random fields (MTCRFs) model to discover the sequence-specific structure and the sequence-shared relationship. Specifically, the multi-chain graph structure and the corresponding probabilistic model are designed to represent the interaction among multiple part-induced action unit sequences. Moreover we propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D.

[Comparison between mini-traumatic bone-grafting and non-bone-grafting in percutaneous K-wire fixation to treat the calcaneal fractures].

OBJECTIVE: To compare the effect between mini-traumatic bone-grafting and non-bone-grafting in percutaneous K-wire fixation for treating the calcaneal fractures. METHODS: From 2002 to 2006, 112 patients with the type II (Paley type) fractures of calcaneus were studied. There were 56 cases in bone-grafting group involving 36 males and 20 famales,aged from 21 to 65, averaged (42.0 +/- 2.3) years; 11 cases were in type II a and 45 were in type II b; the course was from 3 to 14 days, averaged (6.0 +/- 1.2) days. And there were 56 cases in non-bone-grafting group involving 38 males and 18 famales,aged from 22 to 67, averaged (43.0 +/- 2.5)years; 13 cases were in type II a and 43 were in type II b; the course was from 2 to 15 days, averaged (5.0-2.1) days. All the cases were treated by closed reduction and percutaneous K-wire fixation, and bone-grafting group(56 cases) were treated by mini-traumatic bone-grafting, but the other group (56 cases) were not. The collapsing rate and fineness rate were compared. RESULTS: All the cases were followed up from 5 to 52 months. There were no collapsing cases in the bone-grafting group after operation, but 3 cases occurrenced re-collapsing in the non-bone-grafting group. According to the Zhang Tie-liang's evaluation criterion, in the bone-grafting group,the results were excellent in 43 cases, good in 12, fair in 1, the fineness rate was 98.2%. In the non-bone-grafting group,the results were excellent in 37 cases, good in 16, fair in 2, poor in 1, the fineness rate was 94.7%. CONCLUSION: Treatment of the type II fracture of calcaneus with closed reduction, percutaneous K-wire fixation and mini-traumatic bone-grafting can prevent the posterior talar articular surface of caltaneus from collapsing again after operation, enhance the union of fracture, elevate the curative effect, thus it should be taken with the standard therapeutic regimen.