Exploring the Impact of Learning Paradigms on Network Generalization: A Multi-Center IMT Study

The intima-media thickness (IMT) is an important parameter for evaluating cardiovascular disease risk and progression and can be extracted from B-mode longitudinal ultrasound images of the carotid artery. Despite its clinical significance, inter- and intra-operator variability in IMT measurement is a challenge due to subjective factors. Therefore, automatic and semi-automatic approaches based on heuristic methods and deep neural networks have been proposed to reduce the variability in IMT measurement. However, the inter- and intra- operator variability still remains an issue as it affects the quality and diversity of ground truth (GT) data used for training deep learning models. In this study, the authors evaluate the performance of different learning paradigms using different GTs on a multi-center IMT dataset. A recent segmentation network, ConvNeXt, is trained on a dataset of 2576 B-mode longitudinal ultrasound images of the carotid artery, using different GT annotations and learning paradigms. The method is then tested on an external dataset of 448 images from four different centers for which three manual segmentations were available. The results show how the use of different GT annotations and learning paradigms can enhance the generalization ability of deep learning models, demonstrating the importance of selecting appropriate GT data and learning strategies in achieving robust and reliable solutions. The study highlights the significance of incorporating heuristic methods in the training process of deep learning models to enhance the accuracy and consistency of IMT measurement, thus enabling more precise cardiovascular disease risk assessment

Automatic segmentation of the optic nerve in transorbital ultrasound images using a deep learning approach

Transorbital sonography is able to provide reliable information about (a) intra-cranial pressure estimation through the optic nerve sheath diameter (ONSD) measurement, and (b) optic nerve atrophy in patients with multiple sclerosis through the optic nerve diameter (OND). In this study, we present the first method for the automatic measurement of the OND and ONSD using a deep learning technique (UNet with ResNet50 encoder) for the optic nerve segmentation. The dataset included 201 images from 50 patients. The automated measurements were compared with manual ones obtained by one operator. The mean error was equal to 0.07 ± 0.34 mm and -0.07 ± 0.67 mm, for the OND and ONSD, respectively. The developed system should aid in standardizing OND and ONSD measurements and reduce manual evaluation variability

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

Innovative temporal loss function for segmentation of fine structures in ultrasound images

Over the past few years, there have been significant advancements in deep learning architectures for semantic segmentation. However, the performance of these models heavily relies on the loss function (LF) used during network training. The LF is a crucial component that enables the network to weigh the errors differently based on the segmentation task to be performed. Despite the progress made in designing increasingly complex and deep architectures for semantic segmentation, the LFs used in these models have remained almost unchanged. Accurately segmenting small and fine objects, such as vessel walls (e.g., intima-media complex, IMC) or nerves (e.g., optic nerve), in ultrasound (US) images is still a challenging task. One of the main difficulties is pixel imbalance between the object and the background, which can result in inaccurate segmentation. Additionally, precise and accurate segmentation along the object's edge is crucial for medical diagnosis and treatment. To address these challenges, this paper proposes a new, temporal loss function for semantic segmentation in US images. The idea behind a temporal loss is to enable the network to learn from multiple sources of information simultaneously and to give more emphasis to losses that are more informative at different stages of the training process. The proposed LF considers pixel imbalance between the object and background and enables precise and accurate segmentation along the object's edge. The study aims to demonstrate the effectiveness of the proposed LF by evaluating its performance in segmenting vessel walls in US images

Changes in supramaximal M-wave amplitude at different regions of biceps brachii following eccentric exercise of the elbow flexors

Purpose Previous evidence from surface electromyograms (EMGs) suggests that exercise-induced muscle damage (EIMD) may manifest unevenly within the muscle. Here we investigated whether these regional changes were indeed associated with EIMD or if they were attributed to spurious factors often afecting EMGs. Methods Ten healthy male subjects performed 3×10 eccentric elbow fexions. Maximal voluntary contraction (MVC), muscle soreness and ultrasound images from biceps brachii distal and proximal regions were measured immediately before (baseline) and during each of the following 4 days after the exercise. Moreover, 64 monopolar surface EMGs were detected while 10 supramaximal pulses were applied to the musculocutaneous nerve. The innervation zone (IZ), the number of electrodes detecting largest M-waves and their centroid longitudinal coordinates were assessed to characterize the spatial distribution of the M-waves amplitude. Results The MVC torque decreased (~25%; P<0.001) while the perceived muscle soreness scale increased (~4 cm; 0 cm for no soreness and 10 cm for highest imaginable soreness; P<0.005) across days. The echo intensity of the ultrasound images increased at 48 h (71%), 72 h (95%) and 96 h (112%) for both muscle regions (P<0.005), while no diferences between regions were observed (P=0.136). The IZ location did not change (P=0.283). The number of channels detecting the greatest M-waves signifcantly decreased (up to 10.7%; P<0.027) and the centroid longitudinal coordinate shifted distally at 24, 48 and 72 h after EIMD (P<0.041). Conclusion EIMD consistently changed supramaximal M-waves that were detected mainly proximally from the biceps brachii, suggesting that EIMD takes place locally within the biceps brachii

Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

Background Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model. Method In this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected. Results Our strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement). Conclusion We strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS)

CAROTIDNet: A Novel Carotid Symptomatic/Asymptomatic Plaque Detection System Using CNN-Based Tangent Optimization Algorithm in B-Mode Ultrasound Images

Deep learning methods have shown promise for automated medical image analysis tasks. However, class imbalance is a common challenge that can negatively impact model performance, especially for tasks with minority classes that are clinically significant. This study aims to address this challenge through a novel hyperparameter optimization technique for training convolutional neural networks on imbalanced data. We developed a custom Convolutional Neural Network (CNN) architecture and introduced a Tangent Optimization Algorithm (TOA) based on the trigonometric properties of the tangent function. The TOA optimizes hyperparameters during training without requiring data preprocessing or augmentation steps. We applied our approach to classifying B-mode ultrasound carotid artery plaque images as symptomatic or asymptomatic using a dataset with significant class imbalance. On k-fold cross-validation, our method achieved an average accuracy of 98.82%, a sensitivity of 99.41%, and a specificity of 95.74%. The proposed optimization technique provides a computationally efficient and interpretable solution for training deep learning models on unbalanced medical image datasets

Non-Invasive Analysis of Actinic Keratosis before and after Topical Treatment Using a Cold Stimulation and Near-Infrared Spectroscopy

Background and objectives: The possible evolution of actinic keratoses (AKs) into invasive squamous cell carcinomas (SCC) makes their treatment and monitoring essential. AKs are typically monitored before and after treatment only through a visual analysis, lacking a quantitative measure to determine treatment effectiveness. Near-infrared spectroscopy (NIRS) is a non-invasive measure of the relative change of oxy-hemoglobin and deoxy-hemoglobin (O2Hb and HHb) in tissues. The aim of our study is to determine if a time and frequency analysis of the NIRS signals acquired from the skin lesion before and after a topical treatment can highlight quantitative differences between the AK skin lesion area. Materials and Methods: The NIRS signals were acquired from the skin lesions of twenty-two patients, with the same acquisition protocol: baseline signals, application of an ice pack near the lesion, removal of ice pack and acquisition of vascular recovery. We calculated 18 features from the NIRS signals, and we applied multivariate analysis of variance (MANOVA) to compare differences between the NIRS signals acquired before and after the therapy. Results: The MANOVA showed that the features computed on the NIRS signals before and after treatment could be considered as two statistically separate groups, after the ice pack removal. Conclusions: Overall, the NIRS technique with the cold stimulation may be useful to support non-invasive and quantitative lesion analysis and regression after a treatment. The results provide a baseline from which to further study skin lesions and the effects of various treatments

Quantitative Assessment of Cancer Vascular Architecture by Skeletonization of High-resolution 3-D Contrast-enhanced Ultrasound Images: Role of Liposomes and Microbubbles.

The accurate characterization and description of the vascular network of a cancer lesion is of paramount importance in clinical practice and cancer research in order to improve diagnostic accuracy or to assess the effectiveness of a treatment. The aim of this study was to show the effectiveness of liposomes as an ultrasound contrast agent to describe the 3-D vascular architecture of a tumor. Eight C57BL/6 mice grafted with syngeneic B16-F10 murine melanoma cells were injected with a bolus of 1,2-Distearoyl-sn-glycero-3-phosphocoline (DSPC)-based non-targeted liposomes and with a bolus of microbubbles. 3-D contrast-enhanced images of the tumor lesions were acquired in three conditions: pre-contrast, after the injection of micro bubbles, and after the injection of liposomes. By using a previously developed reconstruction and characterization image processing technique, we obtained the 3-D representation of the vascular architecture in these three conditions. Six descriptive parameters of these networks were also computed: the number of vascular trees (NT), the vascular density (VD), the number of branches, the 2-D curvature measure, the number of vascular flexes of the vessels, and the 3-D curvature. Results showed that all the vascular descriptors obtained by liposome-based images were statistically equal to those obtained by using microbubbles, except the VD which was found to be lower for liposome images. All the six descriptors computed in pre-contrast conditions had values that were statistically lower than those computed in presence of contrast, both for liposomes and microbubbles. Liposomes have already been used in cancer therapy for the selective ultrasound-mediated delivery of drugs. This work demonstrated their effectiveness also as vascular diagnostic contrast agents, therefore proving that liposomes can be used as efficient “theranostic” (i.e. therapeutic 1 diagnostic) ultrasound probes

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions