Automatic spondylolisthesis grading from MRIs across modalities using faster adversarial recognition network.

In object detection algorithms, the bounding box regression (BBR) loss directly influences the model's accuracy in predicting object positions. Initially, we conducted simulation experiments on the proposed intersection over union-based loss. Through in-depth analysis, we discovered that the width and height components of the distance term could hinder BBR. To address this, we proposed the AIoU-v1 loss, which decouples the width and height components of the distance term, thereby preventing the suppression of BBR loss by the distance term. Additionally, issues such as the imbalance between sample quantity and sample quality in the dataset, as well as labeling errors, can adversely affect BBR. To tackle these dataset problems, we designed an adaptive dynamic non-monotonic focusing mechanism with strong robustness and wide applicability. Finally, we proposed a post-processing algorithm that combines fusion and non-maximum suppression, resulting in more accurate bounding boxes during the post-processing stage. Our source code and data are available at https://github.com/Wenhua-Zheng/AIOU.

Object detection is a fundamental task in computer vision. Bounding box regression (BBR) losses are critical to detector performance. However, evaluation measures that rely on the Intersection over Union (IoU) between the predicted and ground truth boxes are highly sensitive to positional deviations, which can hinder optimization. To alleviate this issue, we propose ATDIoU, a novel arctangent-differential loss for bounding-box regression. ATDIoU computes distance similarity between a predicted and a ground truth box by modeling the distances between their corresponding vertices as a two-dimensional arctangent differential distribution (ATD). This arctangent differential-based design mitigates bounding box drift and reduces sensitivity to localization errors. As a result, it guides the model to learn target positions more effectively. We evaluate ATDIoU by integrating it into YOLOv6 and conducting experiments on PASCAL VOC and VisDrone2019. The results demonstrate that ATDIoU yields improvements of 1.4% and 0.7% in mean average precision (mAP) relative to MPDIoU.

Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

Medical image diagnosis and delineation of lesions in the human brain require information to combine from different imaging sensors. Image registration is considered to be an essential pre-processing technique of aligning images of different modalities. The brain is a naturally bilateral symmetrical organ, where the left half lobe resembles the right half lobe around the symmetrical axis. The identified symmetry axis in one MRI image can identify symmetry axes in multi-modal registered MRI images instantly. MRI sensors may induce different levels of noise and Intensity Non-Uniformity (INU) in images. These image degradations may cause difficulty in finding true transformation parameters for an optimization technique. We will be investigating the new variant of evolution strategy of genetic algorithm as an optimization technique that performs well even for the high level of noise and INU, compared to Nesterov, Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (LBFGS), Simulated Annealing (SA), and Single-Stage Genetic Algorithm (SSGA). The proposed new multi-modal image registration technique based on a genetic algorithm with increasing precision levels and decreasing search spaces in successive stages is called the Multi-Stage Forward Path Regenerative Genetic Algorithm (MFRGA). Our proposed algorithm is better in terms of overall registration error as compared to the standard genetic algorithm. MFRGA results in a mean registration error of 0.492 in case of the same level of noise (1–9)% and INU (0–40)% in both reference and template image, and 0.317 in case of a noise-free template and reference with noise levels (1–9)% and INU (0–40)%. Accurate registration results in good segmentation, and we apply registration transformations to segment normal brain structures for evaluating registration accuracy. The brain segmentation via registration with our proposed algorithm is better even in cases of high levels of noise and INU as compared to GA and LBFGS. The mean dice similarity coefficient of brain structures CSF, GM, and WM is 0.701, 0.792, and 0.913, respectively.

DeforT: Deformable transformer for visual tracking

Clinical diagnosis, as well as therapy planning and evaluation, are increasingly supported by multimodal images. There are many instances desiring integration of the information obtained by various imaging devices. This paper describes a new approach to match images of different modalities. Differential operators are used in combination with Gaussian blurring to extract geometric features from the images that correspond to similar structures. The resulting 'feature' images may be used with existing matching techniques that minimize the distance between the features in the images to be matched. Our first application of this new approach concerns matching of MRI and CT brain images. The so-called L upsilon upsilon operator produces a ridge-like feature image from which in CT and MRI the center curve of the cranium is easily extracted. First results of this operator's performance in matching tasks are shown. Another promising operator is the 'umbilicity' operator, which is presented in combination with SPECT images.

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.

High capacity data hiding schemes for medical images based on difference expansion

Recent studies on Parkinson's Disease (PD) discovered potential markers in brain MR images for its diagnosis and staging.One of them is the neuromelanin, which can be found in regions such as the substantia nigra and the locus coeruleus, both located in the brainstem.The analysis of these regions is normally conducted manually by a specialized professional, which can be costly and timeconsuming.Fortunately, there have been advances in automatic segmentation approaches in several areas.In medical imaging, U-Nets are showing state-of-the-art performance, but we did not find many works using this architecture to extract brainstem structures.A likely cause for this is the scarcity of large databases with well annotated segmentation of these areas.Thus, this paper explores the use of a dataset of our research group composed of T1-weighted MR images to train U-Net models with different types of slices to automatically demarcate the brainstem (as a first stage).The analysis starts with a scripted segmentation of the target region using the Freesurfer package.Thereby, we trained 4 models and evaluated them using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU).Three models were trained on single anatomical planes, and one on multi-plane slices.Also, we applied grid search, varying optimizers, numbers of filters in the first layer, and learning rates.The best model trained with the larger subset with the best performance was the axial (DSC: 91.76% and IoU: 86.95%), followed by sagittal (DSC: 90.61% and IoU: 84.11%), coronal (DSC: 89.38% and IoU: 68.76%), and all slices (DSC: 16.36% and IoU: 7.58%).The next steps of our research are to differentiate the midbrain out of the brainstem, considering manual segmentation as ground truth, and test some approaches on determining the threshold and evaluating separately the 2 hemispheres of the midbrain.

Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients

Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques

Accurate bounding-box regression with distance-IoU loss for visual tracking

Arbitrary-oriented objects exist widely in remote sensing images. The mainstream rotation detectors use oriented bounding boxes (OBBs) or quadrilateral bounding boxes (QBBs) to represent the rotating objects. However, these methods suffer from the representation ambiguity for oriented object definition, which leads to suboptimal regression optimization and the inconsistency between the loss metric and the localization accuracy of the predictions. In this letter, we propose a representation invariance loss (RIL) to optimize the bounding box regression for the rotating objects in the remote sensing images. RIL treats multiple representations of an oriented object as multiple equivalent local minima and hence transforms bounding box regression into an adaptive matching process with these local minima. Next, the Hungarian matching algorithm is adopted to obtain the optimal regression strategy. Besides, we propose a normalized rotation loss to alleviate the weak correlation between different variables and their unbalanced loss contribution in OBB representation. Extensive experiments on remote sensing datasets show that our method achieves consistent and substantial improvement. The code and models are available at <uri>https://github.com/ming71/RIDet</uri> to facilitate future research.

Loss functions, such as the IoU Loss function and the GIoU (Generalized Intersection over Union) Loss function have been put forward to replace regression loss functions commonly used in regression loss calculation. GIoU Loss alleviates the vanishing gradient in the case of the non-overlapping, but it will completely degenerate into the IoU Loss function when bounding boxes overlap totally, which fails to achieve the optimization effect. To solve this problem, some improvements are proposed in this paper on the basis of the GIoU Loss function, taking into account the overlap rate of complete overlap of bounding boxes. In PASCAL VOC data, the experimental results demonstrate that the AP of NGIoU Loss function in the YOLOv4 model is 47.68%, 1.15% higher than that of the GIoU Loss function, and the highest map value is 86.79% in the YOLOv5 model.

Object detection (OD) in unmanned aerial vehicle (UAV) images faces many challenges, with diverse-scale objects and small objects being particularly prominent issues. To alleviate these challenges, we propose a novel multiscale feature learning and feature fusion network under the guidance of deformable convolution. First, a deformable convolution-guided feature learning (DCGFL) block is designed in the backbone to extract more effective multiscale features. The DCGFL block leverages the adaptability of deformable convolution to the shapes and scales of objects, akin to spatial attention. Moreover, it also employs channel attention to identify important feature maps. Hence, the proposed backbone possesses the functionality of spatial attention and channel attention. Second, in the neck, we devise a simple generalized feature pyramid network (SimpleGFPN) with several deformable convolution-guided feature fusion (DCGFF) blocks to fuse multiscale features. The proposed neck has cross-layer and cross-scale pathways, facilitating effective information exchange and fusion between shallow spatial and deep semantic features. Third, the SIoU loss is used to better model the bounding box regression loss. Finally, experimental results on the VisDrone2021 and UAVDT datasets show that the proposed method outperforms the compared OD methods. In terms of mean average precision, we obtain 37.8% on VisDrone2021 and 18.5% on UAVDT.