Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study.

We aimed to develop machine learning (ML)-based algorithms to assist physicians in ultrasound-guided localization of cricoid cartilage (CC) and thyroid cartilage (TC) in cricothyroidotomy. Adult female volunteers were prospectively recruited from two hospitals between September and December, 2020. Ultrasonographic images were collected via a modified longitudinal technique. You Only Look Once (YOLOv5s), Faster Regions with Convolutional Neural Network features (Faster R-CNN), and Single Shot Detector (SSD) were selected as the model architectures. A total of 488 women (mean age: 36.0 years) participated in the study, contributing to a total of 292,053 frames of ultrasonographic images. The derived ML-based algorithms demonstrated excellent discriminative performance for the presence of CC (area under the receiver operating characteristic curve [AUC]: YOLOv5s, 0.989, 95% confidence interval [CI]: 0.982-0.994; Faster R-CNN, 0.986, 95% CI: 0.980-0.991; SSD, 0.968, 95% CI: 0.956-0.977) and TC (AUC: YOLOv5s, 0.989, 95% CI: 0.977-0.997; Faster R-CNN, 0.981, 95% CI: 0.965-0.991; SSD, 0.982, 95% CI: 0.973-0.990). Furthermore, in the frames where the model could correctly indicate the presence of CC or TC, it also accurately localized CC (intersection-over-union: YOLOv5s, 0.753, 95% CI: 0.739-0.765; Faster R-CNN, 0.720, 95% CI: 0.709-0.732; SSD, 0.739, 95% CI: 0.726-0.751) or TC (intersection-over-union: YOLOv5s, 0.739, 95% CI: 0.722-0.755; Faster R-CNN, 0.709, 95% CI: 0.687-0.730; SSD, 0.713, 95% CI: 0.695-0.730). The ML-based algorithms could identify anatomical landmarks for cricothyroidotomy in adult females with favorable discriminative and localization performance. Further studies are warranted to transfer this algorithm to hand-held portable ultrasound devices for clinical use.

Towards Adversarial Robustness for Multi-Mode Data through Metric Learning.

Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial training, which is currently one of the most effective defense methods, mainly focus on the single-mode setting and thus fail to capture the full data representation to defend against adversarial attacks. To confront this challenge, we propose a novel multi-prototype metric learning regularization for adversarial training which can effectively enhance the defense capability of adversarial training by preventing the latent representation of the adversarial example changing a lot from its clean one. With extensive experiments on CIFAR10, CIFAR100, MNIST, and Tiny ImageNet, the evaluation results show the proposed method improves the performance of different state-of-the-art adversarial training methods without additional computational cost. Furthermore, besides Tiny ImageNet, in the multi-prototype CIFAR10 and CIFAR100 where we reorganize the whole datasets of CIFAR10 and CIFAR100 into two and ten classes, respectively, the proposed method outperforms the state-of-the-art approach by 2.22% and 1.65%, respectively. Furthermore, the proposed multi-prototype method also outperforms its single-prototype version and other commonly used deep metric learning approaches as regularization for adversarial training and thus further demonstrates its effectiveness.

Cauda equina syndrome caused by the application of DuraSealTM in a microlaminectomy surgery: A case report.

BACKGROUND: The management of dural tears is important. While a massive dura can be repaired with absorbable suture lines, cerebrospinal fluid leakage can be attenuated by dural sealant when an unintended tiny durotomy occurs intraoperatively. DuraSeal is often used because it can expand to seal tears. This case emphasizes the need for caution when DuraSeal is used as high expansion can cause complications following microlaminectomy. CASE SUMMARY: A 77-year-old woman presented with L2/3 and L3/4 lateral recess stenosis. She underwent microlaminectomy, foraminal decompression, and disk height restoration using an IntraSPINE® device. A tiny incident durotomy occurred intraoperatively and was sealed using DuraSealTM. However, decreased muscle power, urinary incontinence, and absence of anal reflexes were observed postoperatively. Emergent magnetic resonance imaging revealed fluid collection causing thecal sac indentation and central canal compression. Surgical exploration revealed that the gel-like DuraSeal had entrapped the hematoma and, consequently, compressed the thecal sac and nerve roots. While we removed all DuraSealTM and exposed the nerve root, the patient's neurological function did not recover postoperatively. CONCLUSION: DuraSeal expansion must not be underestimated. Changes in neurological status require investigation for cauda equina syndrome due to expansion.

Degree of Pelvic Rotation in the Coronal Plane on Postoperative Radiographs Obtained after Total Hip Arthroplasty.

There are many published cup anteversion measurements for postoperative total hip arthroplasty (THA), including Liaw's, Lewinnek's, and Murray's methods. However, most measurements ignore the potential pelvic rotation on radiographs except in Liaw's method. Without considering pelvic rotation, clinicians can miscalculate cup anteversion. Therefore, we aimed to quantify the mean degree of pelvic rotation. Herein, we collected 388 radiographs of 98 postoperative THA hips of 77 patients and measured pelvic rotation by measuring h, the horizontal displacement of the sacrococcygeal junction associated with the upper pole of the symphysis pubis, and ssd, the distance between the sacrococcygeal junction and pubic symphysis. The angle θ of pelvic rotation was defined as θ = arc sin (h/ssd) × (180°/π). The mean degree of pelvic rotation was then calculated. The standard deviation of h was 7.84 mm, and the mean ssd was 158 mm. The potential pelvic rotation was 2.50°. The p-values from the paired t-test were all >0.05 when interobserver and intraobserver errors were assessed. This is the first study to quantify the potential pelvic rotation in the coronal plane on postoperative plain radiographs. The potential pelvic rotation was too large to be neglected during the measurement of cup anteversion.

Successful Plate Fixation with Long Intramedullary Fibula Bone Graft for Periprosthetic Femur Fracture: A Case Report.

Background and objectives: Treatment of a displaced or comminuted periprosthetic distal femur fracture is challenging, especially in patients with osteoporosis. In this case report, we shared our successful surgical experience of using a long intramedullary fibula bone graft in a plate fixation surgery for a periprosthetic distal femur fracture in an extremely elderly patient with osteoporosis. Case report: A 95-year-old woman with severe osteoporosis (bone mineral density level: -3.0) presented with right knee pain and deformity after a fall, and a right periprosthetic distal femur fracture was identified. The patient underwent an open reduction and an internal plate fixation surgery with the application of a long intramedullary fibular bone graft. Due to a solid fixation, immediate weight-bearing was allowed after the surgery. She could walk independently without any valgus or varus malalignment or shortening 3 months after the surgery. A solid union was achieved 4 months postoperatively. Conclusions: We present a case wherein a long intramedullary allogenous fibula strut bone graft was used successfully to treat a right periprosthetic femur fracture in an extremely elderly patient. A long allogenous fibula bone graft can act not only as a firm structure for bridging the bone defect but also as a guide for precise component alignment. We believe this treatment option for periprosthetic fractures is beneficial for achieving biological and mechanical stability and facilitates early mobilization and weight-bearing for the patient.

Learning Binary Hash Codes Based on Adaptable Label Representations.

The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing. In this article, we introduce Adaptive Labeling Deep Hashing (AdaLabelHash) that learns binary hash codes based on learnable class label representations. We treat the class labels as the vertices of a K -dimensional hypercube, which are trainable variables and adapted together with network weights during the backward network training procedure. The label representations, referred to as codewords, are the target outputs of hash mapping learning. In the label space, semantically relevant images are then expressed by the codewords that are nearby regarding Hamming distances, yielding compact and discriminative binary hash representations. Furthermore, we find that the learned label representations well reflect semantic relations. Our approach is easy to realize and can simultaneously construct both the label representations and the compact binary embeddings. Quantitative and qualitative evaluations on several popular benchmarks validate the superiority of AdaLabelHash in learning effective binary codes for image search.

Cross-Batch Reference Learning for Deep Retrieval.

Learning effective representations that exhibit semantic content is crucial to image retrieval applications. Recent advances in deep learning have made significant improvements in performance on a number of visual recognition tasks. Studies have also revealed that visual features extracted from a deep network learned on a large-scale image data set (e.g., ImageNet) for classification are generic and perform well on new recognition tasks in different domains. Nevertheless, when applied to image retrieval, such deep representations do not attain performance as impressive as used for classification. This is mainly because the deep features are optimized for classification rather than for the desired retrieval task. We introduce the cross-batch reference (CBR), a novel training mechanism that enables the optimization of deep networks with a retrieval criterion. With the CBR, the networks leverage both the samples in a single minibatch and the samples in the others for weight updates, enhancing the stochastic gradient descent (SGD) training by enabling interbatch information passing. This interbatch communication is implemented as a cross-batch retrieval process in which the networks are trained to maximize the mean average precision (mAP) that is a popular performance measure in retrieval. Maximizing the cross-batch mAP is equivalent to centralizing the samples relevant to each other in the feature space and separating the samples irrelevant to each other. The learned features can discriminate between relevant and irrelevant samples and thus are suitable for retrieval. To circumvent the discrete, nondifferentiable mAP maximization, we derive an approximate, differentiable lower bound that can be easily optimized in deep networks. Furthermore, the mAP loss can be used alone or with a classification loss. Experiments on several data sets demonstrate that our CBR learning provides favorable performance, validating its effectiveness.

Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks.

This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.

A learning framework for age rank estimation based on face images with scattering transform.

This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age estimation based on face images. The proposed approach exploits relative-order information among the age labels for rank prediction. In our approach, the age rank is obtained by aggregating a series of binary classification results, where cost sensitivities among the labels are introduced to improve the aggregating performance. In addition, we give a theoretical analysis on designing the cost of individual binary classifier so that the misranking cost can be bounded by the total misclassification costs. An efficient descriptor, scattering transform, which scatters the Gabor coefficients and pooled with Gaussian smoothing in multiple layers, is evaluated for facial feature extraction. We show that this descriptor is a generalization of conventional bioinspired features and is more effective for face-based age inference. Experimental results demonstrate that our method outperforms the state-of-the-art age estimation approaches.

Intrinsic illumination subspace for lighting insensitive face recognition.

We introduce the intrinsic illumination subspace and its application for lighting insensitive face recognition in this paper. The intrinsic illumination subspace is constructed from illumination images of intrinsic images, which is a midlevel description of appearance images and can be useful for many visual inferences. This subspace forms a convex polyhedral cone and can be efficiently represented by a low-dimensional linear subspace, which enables an analytic generation of illumination images under varying lighting conditions. When only objects of the same class, such as faces, are concerned, a class-based generic intrinsic illumination subspace can be constructed in advance and used for novel objects of the same class. Based on this class-based generic subspace, we propose a lighting normalization method for lighting insensitive face recognition, where only a single input image is required. The generic subspace is used as a bootstrap subspace for illumination images of novel objects. Face recognition experiments are performed to demonstrate the effectiveness of the proposed lighting normalization method and verify empirically that the class-based generic subspace is applicable to novel objects. Our method is simple and fast, which makes it useful for real-time applications, embedded systems, or mobile devices with limited resources.

Tracking by parts: a bayesian approach with component collaboration.

Instead of using global-appearance information for visual tracking, as adopted by many methods, we propose a tracking-by-parts (TBP) approach that uses partial appearance information for the task. The proposed method considers the collaborations between parts and derives a probability propagation framework by encoding the spatial coherence in a Bayesian formulation. To resolve this formulation, a TBP particle-filtering method is introduced. Unlike existing methods that only use the spatial-coherence relationship for particle-weight estimation, our method further applies this relationship for state prediction based on system dynamics. Thus, the part-based information can be utilized efficiently, and the tracking performance can be improved. Experimental results show that our approach outperforms the factored-likelihood and particle reweight methods, which only use spatial coherence for weight estimation.

Visual tracking in high-dimensional state space by appearance-guided particle filtering.

In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a particle filtering framework is developed to realize it. Experimental results demonstrate that the proposed method is effective for high degree-of-freedom visual tracking problems, such as articulated hand tracking and lip-contour tracking.

Fast human detection using a novel boosted cascading structure with meta stages.

We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based 1-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the classifiers from the training images for each stage of the cascaded structure. Instead of using the standard boosted cascade, the proposed method employs a novel cascaded structure that exploits both the stage-wise classification information and the interstage cross-reference information. We introduce meta-stages to enhance the detection performance of a boosted cascade. Experiment results show that the proposed approach achieves high detection accuracy and efficiency.

A new approach to image copy detection based on extended feature sets.

Conventional image copy detection research concentrates on finding features that are robust enough to resist various kinds of image attacks. However, finding a globally effective fealure is difficult and, in many cases, domain dependent. Instead of imply extracting features from copyrighted images directly, we propose a new framework called the extended feature set for detecting copies of images. In our approach, virtual prior attacks are applied to copyrighted images to generate novel features, which serve as training data. The copy-detection problem can be solved by learning classifiers from the training data, thus, generated. Our approach can be integrated into existing copy detectors to further improve their performance. Experiment results demonstrate that the proposed approach can substantially enhance the accuracy of copy detection.

Panoramic appearance-based recognition of video contents using matching graphs.

This paper proposes a general scheme for recognizing the contents of a video using a set of panoramas recorded in a database. In essence, a panorama inherently records the appearances of an omni-directional scene from its central point to arbitrary viewing directions and, thus, can serve as a compact representation of an environment. In particular, this paper emphasizes the use of a sequence of successive frames in a video taken with a video camera, instead of a single frame, for visual recognition. The associated recognition task is formulated as a shortest-path searching problem, and a dynamic-programming technique is used to solve it. Experimental results show that our method can effectively recognize a video.

Reducing SVM classification time using multiple mirror classifiers.

We propose an approach that uses mirror point pairs and a multiple classifier system to reduce the classification time of a support vector machine (SVM). Decisions made with multiple simple classifiers formed from mirror pairs are integrated to approximate the classification rule of a single SVM. A coarse-to-fine approach is developed for selecting a given number of member classifiers. A clustering method, derived from the similarities between classifiers, is used for a coarse selection. A greedy strategy is then used for fine selection of member classifiers. Selected member classifiers are further refined by finding a weighted combination with a perceptron. Experiment results show that our approach can successfully speed up SVM decisions while maintaining comparable classification accuracy.

On pose recovery for generalized visual sensors.

With the advances in imaging technologies for robot or machine vision, new imaging devices are being developed for robot navigation or image-based rendering. However, to satisfy some design criterion, such as image resolution or viewing ranges, these devices are not necessarily being designed to follow the perspective rule and, thus, the imaging rays may not pass through a common point. Such generalized imaging devices may not be perspective and, therefore, their poses cannot be estimated with traditional techniques. In this paper, we propose a systematic method for pose estimation of such a generalized imaging device. We formulate it as a nonperspective n point (NPnP) problem. The case with exact solutions, n=3, is investigated comprehensively. Approximate solutions can be found for n>3 in a least-squared-error manner by combining an initial-pose-estimation procedure and an orthogonally iterative procedure. This proposed method can be applied not only to nonperspective imaging devices but also perspective ones. Results from experiments show that our approach can solve the NPnP problem accurately.

Fuzzy kernel perceptron.

A new learning method, the fuzzy kernel perceptron (FKP), in which the fuzzy perceptron (FP) and the Mercer kernels are incorporated, is proposed in this paper. The proposed method first maps the input data into a high-dimensional feature space using some implicit mapping functions. Then, the FP is adopted to find a linear separating hyperplane in the high-dimensional feature space. Compared with the FP, the FKP is more suitable for solving the linearly nonseparable problems. In addition, it is also more efficient than the kernel perceptron (KP). Experimental results show that the FKP has better classification performance than FP, KP, and the support vector machine.