Can multiple segmentation methods enhance deep learning networks generalization? A novel hybrid learning paradigm

Deep learning methods are the state-of-the-art for medical imaging segmentation tasks. Still, numerous segmentation algorithms based on heuristic-based methods have been proposed with exceptional results. To validate segmentation algorithms, manual annotations are typically considered as ground truth. However, manual annotations often suffer from inter/intra-operator variability and can also be occasionally inaccurate, especially when considering time-consuming and precise tasks. A sample case is the manual delineation of the lumen-intima (LI) and media-adventitia (MA) borders for intima-media thickness (IMT) measurement in B-mode ultrasound images. In this work, a novel hybrid learning paradigm which combines manual segmentations with the automatic segmentation of a dynamic programming technique for ground truth determination is presented. A profile consensus strategy is proposed to construct the hybrid ground truth. Two open-source datasets (n=2576) were employed for training four deep learning networks using the hybrid learning paradigm and three single source training targets as a comparison. The pipeline was fixed across the four tests and included a Faster R-CNN detection network to locate the carotid artery and then subsequent division into patches which were segmented using a UNet. The validation of the results was performed on an external test set comparing the predictions of the four different models to the annotations of three independent manual operators. The hybrid learning paradigm showed the best overall segmentation results (Dice=0.907±0.037, p<0.001) and demonstrated an exceptional correlation between the mean of three operators and the automatic measure (ICC(2,1)=0.958), demonstrating how the incorporation of heuristic-based segmentation methods within the learning paradigm of a deep neural network can enhance and improve final segmentation performance results

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

Chemical shift imaging at 4.7 tesla of brown adipose tissue.

In vivo distinction between small deposits of brown adipose tissue (BAT) and surrounding tissues may be difficult. In this article, we propose an experiment paradigm, based on techniques of chemical shift magnetic resonance imaging (CSI), which can improve the methods presently available for the study of BAT. Male rats were examined in an imager-spectrometer equipped with a 4.7 T magnet. Proton spectra of isolated BAT deposits showed that both fat and water protons contributed significantly to the genesis of the magnetic resonance signal. An equivocal definition of BAT deposits was obtained by three (respectively, spin-echo, water-selective, and fat-selective) images. The spin-echo (SE), T1-weighted image provided the best anatomical description of the structures. The images selective for fat-protons displayed the degree of lipid accumulation in each area. The images selective for water-protons provided an internal control of adipose tissue localization. The proposed paradigm allows an unequivocal definition of BAT deposits and appears particularly useful in studies where experimental manipulation (i.e., cold acclimation or drug treatment) produces changes in this issue

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

Nutrition, Exercise, and Stress Management for Treatment and Prevention of Psychiatric Disorders. A Narrative Review Psychoneuroendocrineimmunology-Based

Psychoneuroendocrineimmunology (PNEI) brings together knowledge acquired since the 1930s from endocrinology, immunology, neuroscience, and psychology. With PNEI, a model of research and interpretation of health and disease is emerging, which sees the human body as a structured and interconnected unit, where the psychological and biological systems are mutually coordinated. In the PNEI view, many factors could influence mental health, with the endocrine system involved in mediating the effects of environmental stress on mental health and inflammation in the onset and course of psychiatric disorders as a result of individual and collective conditions and behaviors. Among these, nutrition is one way by which the environment impacts physiology: indeed, many pieces of research showed that several elements (e.g., probiotics, fish oil, zinc) have a positive effect on mental disorders thus being potentially augmentation agents in treatment. Still, physical activity can moderate depressive symptoms, while prolonged stress increases the risk of psychopathology. Taken together, the PNEI-based approach may inform prevention and treatment strategies, also in the field of mental health care

Role of brain perfusion SPECT with 99mTc HMPAO in the assessment of response to drug therapy in patients with autoimmune vasculitis: a prospective study

Abstract BACKGROUND: The diagnosis of vasculitis in the brain remains a quite difficult achievement. To the best of our knowledge, there is no imaging method reported in literature which is capable of reaching to a diagnosis of vasculitis with very high sensitivity. AIM: The aim of this study was to determine whether perfusion brain single photon emission computed tomography (SPECT) can be usefully employed in monitoring the treatment of vasculitis, allowing treating only potentially responder patients and avoiding the side effects on patients who do not respond. MATERIALS AND METHODS: Twenty patients (two males and 18 females) suffering from systemic lupus erythematosus (SLE; n = 5), Behcet's disease (BD; n = 5), undifferentiated vasculitis (UV; n = 5), and Sjogren's syndrome (SS; n = 5) were included in the study. All patients underwent a wide neurological anamnestic investigation, a complete objective neurological examination and SPECT of the brain with 99mTc-hexamethyl-propylene-aminoxime (HMPAO). The brain SPECT was then repeated after appropriate medical treatment. The neurological and neuropsychiatric follow-up was performed at 6 months after the start of the treatment. RESULTS: Overall, the differences between the scintigraphic results obtained after and before the medical treatment indicated a statistically significant increase of the cerebral perfusion (CP). In 19 out of 200 regions of interest (ROI) studied, the difference between pre- and post treatment percentages had negative sign, indicating a worsening of CP. This latter event has occurred six times (five in the same patients) in the UV, 10 times (eight in the same patients) in the SLE, never in BD, and three times (two in the same patient) in the SS. CONCLUSION: The reported results seem to indicate the possibility of identifying, by the means of a brain SPECT, responder and nonresponder (unchanged or worsened CP) patients, affected by autoimmune vasculitis, to the therapy

Psychometric properties of the Italian body shape questionnaire: an investigation of its reliability, factorial, concurrent, and criterion validity

PURPOSE: This study was set up to investigate the reliability, factorial, concurrent, and criterion validity of the Italian version of the 34-item Body Shape Questionnaire (BSQ) and its shorter versions. METHODS: The study included 231 patients diagnosed with an eating disorder and 58 putatively healthy people (comparison sample). The Italian BSQ-34 was administered to participants together with the Hamilton Depression Rating Scale and the Hamilton Anxiety Rating Scale. Information on body mass index, caloric intake at baseline, and the number of episodes of self-vomiting per week was also acquired. RESULTS: Cronbach’s alpha of BSQ-34 was 0.971 (95% confidence interval [CI] 0.965–0.976) in patients and 0.960 (0.944–0.974) in controls. Test–retest stability in patients (n = 69), measured with intraclass correlation coefficient, was 0.987 (0.983–0.991). Confirmatory factor analysis of the single-factor model yielded acceptable fit for all versions of the BSQ. On all BSQ versions, patients scored higher than controls with a large effect size when calculated as Cliff’s delta. BMI and mean caloric intake at baseline had a stronger association with BSQ-34 than levels of anxiety and depression. The analysis with the receiver operating characteristics (ROC) curve showed that the BSQ-34 distinguished patients with an eating disorder from controls with good accuracy (Area Under the Curve = 86.5; 95% CI 82.2–90.7). CONCLUSION: The Italian version of the BSQ possesses good psychometric properties, in both the long and the shortened versions, and it can be applied to measure body dissatisfaction for both clinical and research purposes. LEVEL OF EVIDENCE: Level III, Evidence obtained from well-designed cohort or case–control analytic studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40519-022-01503-6

Preclinical In vivo Imaging for Fat Tissue Identification, Quantification, and Functional Characterization

Localization, differentiation, and quantitative assessment of fat tissues have always collected the interest of researchers. Nowadays, these topics are even more relevant as obesity (the excess of fat tissue) is considered a real pathology requiring in some cases pharmacological and surgical approaches. Several weight loss medications, acting either on the metabolism or on the central nervous system, are currently under preclinical or clinical investigation. Animal models of obesity have been developed and are widely used in pharmaceutical research. The assessment of candidate drugs in animal models requires non-invasive methods for longitudinal assessment of efficacy, the main outcome being the amount of body fat. Fat tissues can be either quantified in the entire animal or localized and measured in selected organs/regions of the body. Fat tissues are characterized by peculiar contrast in several imaging modalities as for example Magnetic Resonance Imaging (MRI) that can distinguish between fat and water protons thank to their different magnetic resonance properties. Since fat tissues have higher carbon/hydrogen content than other soft tissues and bones, they can be easily assessed by Computed Tomography (CT) as well. Interestingly, MRI also discriminates between white and brown adipose tissue (BAT); the latter has long been regarded as a potential target for anti-obesity drugs because of its ability to enhance energy consumption through increased thermogenesis. Positron Emission Tomography (PET) performed with (18)F-FDG as glucose analog radiotracer reflects well the metabolic rate in body tissues and consequently is the technique of choice for studies of BAT metabolism. This review will focus on the main, non-invasive imaging techniques (MRI, CT, and PET) that are fundamental for the assessment, quantification and functional characterization of fat deposits in small laboratory animals. The contribution of optical techniques, which are currently regarded with increasing interest, will be also briefly described. For each technique the physical principles of signal detection will be overviewed and some relevant studies will be summarized. Far from being exhaustive, this review has the purpose to highlight some strategies that can be adopted for the in vivo identification, quantification, and functional characterization of adipose tissues mainly from the point of view of biophysics and physiology