WISTA-Net, leveraging the strength of the lp-norm, demonstrates superior denoising performance compared to both the classical orthogonal matching pursuit (OMP) algorithm and ISTA within the WISTA paradigm. WISTA-Net demonstrably outperforms the compared methods in denoising efficiency, capitalizing on the high-efficiency of DNN structure parameter updating. On a CPU, processing a 256×256 noisy image with WISTA-Net takes 472 seconds. This is a substantial improvement over the times for WISTA (3288 seconds), OMP (1306 seconds), and ISTA (617 seconds).
To evaluate pediatric craniofacial issues, image segmentation, labeling, and landmark detection are critical steps. Deep neural networks, though recently employed for segmenting cranial bones and locating cranial landmarks in CT or MR images, can be problematic to train, sometimes yielding less-than-ideal results in specific applications. Object detection performance can be enhanced through the utilization of global contextual information, which they rarely leverage. Furthermore, the majority of approaches employ multi-stage algorithms, which are inefficient and prone to errors building up over time. A third consideration is that prevailing strategies often target rudimentary segmentation, with decreased accuracy evident in complex situations, like the labeling of multiple crania in the variable pediatric imaging. A novel end-to-end neural network architecture, built upon the DenseNet framework, is presented in this paper. This network uses context regularization to jointly categorize cranial bone plates and identify cranial base landmarks directly from CT images. We designed a context-encoding module, specifically, to encode global contextual information as landmark displacement vector maps. This encoding guides feature learning for both bone labeling and landmark identification. A diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (aged 0 to 2 years, encompassing 0-63 and 0-54 years), was employed to evaluate our model. The superior performance of our experiments is evident when contrasted with existing cutting-edge approaches.
Remarkable outcomes have been obtained in most medical image segmentation applications using convolutional neural networks. Nonetheless, the inherent localized nature of the convolution process presents constraints in representing long-distance interdependencies. While the sequence-to-sequence globally predictive Transformer was developed to address this issue, its limited capacity for precise positioning may stem from a deficiency in capturing detailed low-level information. Subsequently, low-level features are characterized by rich, granular information, greatly impacting the delineation of organ edges. However, the capacity of a standard CNN model to detect edge information within finely detailed features is limited, and the computational expense of handling high-resolution 3D feature sets is substantial. EPT-Net, an encoder-decoder network, is proposed in this paper to precisely segment medical images; this network combines the insights from edge perception with the capabilities of Transformer architecture. This paper, under this established framework, proposes a Dual Position Transformer for a considerable enhancement in 3D spatial positioning. Biot’s breathing Consequently, recognizing the detailed nature of information in the low-level features, an Edge Weight Guidance module is designed to extract edge information by minimizing the edge information function without adding new parameters to the network. Moreover, the efficacy of the suggested approach was validated on three datasets, including SegTHOR 2019, Multi-Atlas Labeling Beyond the Cranial Vault, and the re-labeled KiTS19 dataset, which we termed KiTS19-M. The findings of the experiments unequivocally demonstrate that EPT-Net's performance in medical image segmentation has substantially advanced beyond the current state-of-the-art.
A multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) may provide substantial support for early diagnosis and interventional management of placental insufficiency (PI), fostering normal pregnancy outcomes. The limitations of existing multimodal analysis methods manifest in their inability to adequately represent multimodal features and define modal knowledge effectively, leading to failures in handling incomplete datasets with unpaired multimodal samples. Recognizing the need to address these challenges and capitalize on the incomplete multimodal data for precise PI diagnosis, we introduce the novel graph-based manifold regularization learning framework named GMRLNet. Utilizing US and MFI images, the process capitalizes on the commonalities and differences in the modalities to create ideal multimodal feature representations. selleck inhibitor Employing a graph convolutional approach, a shared and specific transfer network (GSSTN) is constructed to analyze intra-modal feature associations, enabling the decomposition of each modal input into separable shared and unique feature spaces. To characterize unimodal knowledge, a graph-based manifold approach is applied to describe sample-level feature representations, local inter-sample relations, and the global data distribution pattern within each modality. To achieve effective cross-modal feature representations, an MRL paradigm is then designed for knowledge transfer across inter-modal manifolds. Consequently, MRL's transfer of knowledge between paired and unpaired data enhances the robustness of learning from incomplete datasets. Two clinical datasets were used to assess the performance and generalizability of PI classification using GMRLNet. State-of-the-art evaluations highlight the superior accuracy of GMRLNet when dealing with incomplete datasets. Using our methodology, paired US and MFI images achieved 0.913 AUC and 0.904 balanced accuracy (bACC), while unimodal US images demonstrated 0.906 AUC and 0.888 bACC, highlighting its potential within PI CAD systems.
Introducing a panoramic retinal (panretinal) optical coherence tomography (OCT) imaging system, with a comprehensive 140-degree field of view (FOV). The implementation of a contact imaging approach allowed for faster, more efficient, and quantitative retinal imaging, complete with axial eye length measurement, in order to achieve this unprecedented field of view. The capability of the handheld panretinal OCT imaging system for earlier recognition of peripheral retinal disease has the potential to prevent permanent vision loss. Furthermore, a clear depiction of the peripheral retina promises a deeper insight into disease mechanisms affecting the outer regions of the eye. This manuscript describes a panretinal OCT imaging system with the widest field of view (FOV) currently available among retinal OCT imaging systems, contributing significantly to both clinical ophthalmology and basic vision science.
Noninvasive imaging techniques reveal the morphology and function of microvascular structures deep within tissues, offering valuable data for clinical diagnostics and ongoing patient observation. Cell Biology Microvascular structures are revealed with a subwavelength diffraction resolution by the emerging imaging technique, ultrasound localization microscopy. Nevertheless, the practical application of ULM is hampered by technical constraints, including extended data acquisition durations, substantial microbubble (MB) concentration requirements, and imprecise localization. To perform end-to-end mobile base station localization, we introduce a Swin Transformer-based neural network in this article. Different quantitative metrics were used to verify the performance of the proposed method against both synthetic and in vivo data. Our proposed network, as evidenced by the results, exhibits superior precision and enhanced imaging capabilities compared to prior methodologies. Besides, the computational cost per frame is roughly three to four times faster than existing methods, thereby making the real-time use of this technique plausible in the foreseeable future.
Acoustic resonance spectroscopy (ARS) provides highly accurate determination of structural properties (geometry and material), utilizing the characteristic vibrational modes inherent to the structure. Determining a specific parameter within multibody structures is inherently challenging because of the complex, superimposed resonance peaks present in the vibrational profile. An approach for extracting pertinent features from complex spectra is described, with a focus on isolating resonance peaks that are uniquely sensitive to the targeted property while ignoring noise peaks. We pinpoint specific peaks by employing wavelet transformation, with frequency ranges and wavelet scales optimized through a genetic algorithm. Unlike the conventional wavelet transformation/decomposition, which uses numerous wavelets at diverse scales to represent a signal, including noise peaks, resulting in a considerable feature set and consequently reducing machine learning generalizability, this new method offers a distinct contrast. In detail, we describe the technique, and exhibit its feature extraction application in domains like regression and classification. Genetic algorithm/wavelet transform feature extraction is shown to reduce regression error by 95% and classification error by 40% compared to no feature extraction or the usual wavelet decomposition, a standard approach in optical spectroscopy. A plethora of machine learning techniques can substantially enhance the precision of spectroscopy measurements through effective feature extraction. This discovery will have considerable implications for ARS, in addition to other data-driven spectroscopy techniques, including optical spectroscopy.
Among the primary risk factors for ischemic stroke is carotid atherosclerotic plaque that is prone to rupture, with the risk of rupture fundamentally linked to the plaque's morphology. A noninvasive, in vivo analysis of human carotid plaque composition and structure was achieved via the parameter log(VoA), derived from the decadic logarithm of the second time derivative of displacement induced by an acoustic radiation force impulse (ARFI).