Muscle Segments Research Articles

Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.

Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to March 2023) included 55 healthy volunteers who underwent a noncontrast cardiac MRI examination at 1.5 T. Short-axis stack DL cine sequences of the left ventricle (LV) were performed over one (1RR), three (3RR), and six cardiac (6RR) cycles and compared with a standard cine sequence (without DL, performed over 10-12 cardiac cycles) in regard to acquisition time, subjective image quality, edge sharpness, and volumetric results. Results Total acquisition time (median) for a short-axis stack was 47 seconds for the 1RR cine, 108 seconds for 3RR cine, 184 seconds for 6RR cine, and 227 seconds for the standard sequence. Volumetric results showed no difference for the conventional cine (median LV ejection fraction [EF] 63%), 6RR cine (median LVEF, 62%), and 3RR cine (median LVEF, 61%). The 1RR cine sequence significantly underestimated EF (57%) because of a different segmentation of the papillary muscles. Subjective image quality (P = .37) and edge sharpness (P = .06) of the three-heartbeat DL cine did not differ from the reference standard, while both metrics were lower for single-heartbeat DL cine and higher for six-heartbeat DL cine. Conclusion For DL-based cine sequences, acquisition over three cardiac cycles appears to be the optimal compromise, with no evidence of differences in image quality, edge sharpness, and volumetric results, but with a greater than 50% reduced acquisition time compared with the reference sequence. Keywords: MR Imaging, Cardiac, Heart, Technical Aspects, Cardiac MRI, Deep Learning, Clinical Imaging, Accelerated Imaging Supplemental material is available for this article. © RSNA, 2024.

The comparative and functional anatomy of the forelimb muscle architecture of Humboldt's woolly monkey (Lagothrix lagotricha).

Humboldt's woolly monkey (Lagothrix lagortricha) is a ceboid primate that more frequently engages in plantigrade quadrupedalism (~89%) but is, like most other members of the subfamily Atelinae, capable of suspensory postures and "tail assisted" brachiation. That taxon's decreased reliance on suspension is reflected in the skeletal anatomy of the upper limb which is less derived relative to more frequently suspensory atelines (Ateles, Brachyteles) but is in many ways (i.e., phalangeal curvature, enlarged joint surfaces, elongated diaphyses) intermediate between highly suspensory and quadrupedal anthropoids. Although it has been suggested that muscle may have morphogenetic primacy with respect to bone this has not been explicitly tested. The present study employs analyses of Lagothrix upper limb muscle fiber length, relative physiological cross-sectional area and relative muscle mass to test whether muscular adaptations for suspensory postures and locomotion in Lagothrix precede adaptive refinements in the skeletal tissues or appear more gradually in conjunction with related skeletal adaptations. Results demonstrate that Lagothrix upper limb musculature is most like committed quadrupeds but that limited aspects of the relative distribution of segmental muscle mass may approach suspensory hylobatids consistent with only a limited adaptive response in musculature prior to bone. Results specific to the shoulder were inconclusive owing to under-representation of quadrupedal shoulder musculature and future work should be focused more specifically on the adaptive and functional morphology of the muscular anatomy and microstructure of the scapulothoracic joint complex.

Comparison of Manual vs Artificial Intelligence-Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A.

Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time required for the manual segmentation of individual muscles. This study aimed to evaluate the accuracy and responsiveness of a fully automatic artificial intelligence (AI)-based segmentation pipeline to assess disease progression in a cohort of CMT1A patients over 1 year. Twenty CMT1A patients were included in this observational, prospective, longitudinal study. FF was measured twice a year apart using qMRI in the lower limbs. Individual muscle segmentation was performed fully automatically using a trained convolutional neural network with or without human quality check (QC). The corresponding results were compared with those obtained by fully manual (FM) segmentation using the Dice similarity coefficient (DSC). FF progression and its standardized response mean (SRM) were also computed in individual muscles over the single central slice and a 3D volume to define the most sensitive region of interest. AI-based segmentation showed excellent DSC values (>0.90). Significant global FF progression was observed at thigh (+0.71% ± 1.28%; p = 0.016) and leg (+1.73% ± 2.88%, p = 0.007) levels, similarly to that calculated using the FM technique (p = 0.363 and p = 0.634). FF progression of each individual muscle was comparable when computed from either the central slice or the 3D volume. The best SRM value (0.70) was obtained for the FF progression computed using the AI-based technique with human QC in the 3D volume at the leg level. The time required for fully automatic segmentation using AI with a QC was 10 hours for the entire data set compared with 90 hours for the FM. qMRI combined with AI-based segmentation can be considered as a process ready for assessing longitudinal FF changes in CMT1A patients. Given the slow FF progression at a thigh level and the large heterogeneity between muscles and individuals, FF should be quantified from a 3D volume at the leg level for longitudinal analyses. A QC performed after the AI-based segmentation is still advised given the increased SRM value.

Integrated surgical intervention for intradural extramedullary hemangioblastoma of the cervical spine: a case report and literature review

IntroductionThe incidence of hemangioblastoma is low, constituting only 1-5% of all spinal cord tumors. Specifically, intradural extramedullary hemangioblastoma without Von Hippel-Lindau syndrome represents an exceedingly rare condition.MethodsWe report the first documented case of cervical intradural extramedullary hemangioblastoma in China. A 53-year-old male patient presented with a 3-year history of mild right hemiplegia, segmental muscle strength and sensation impairment, and a positive pyramidal tract sign. MRI showed an abnormal oval signal focus in the intradural and extramedullary region at the C6-C7 vertebral level. Before surgery, angiography was performed to identify the supplying arteries and draining veins. Subsequent interventional therapy achieved over 90% occlusion of blood vessels, creating optimal conditions for complete resection of the spinal tumor.ResultsThe patient demonstrated satisfactory postoperative recovery with significant restoration of sensory and motor functions. Pathological examination showed a significant upregulation of CD31 in tumor cells and a substantial presence of the neuro-specific marker S100 in the tumor stroma, consistent with the diagnostic criteria for spinal hemangioblastoma.ConclusionThe rarity of cervical intradural extramedullary hemangioblastoma without Von Hippel-Lindau syndrome was reaffirmed by a comprehensive review of the existing literature. Complete tumor resection remains the optimal approach for managing this uncommon condition, generally resulting in a favorable prognosis. Traditional open fenestration surgery is linked to elevated risks of bleeding and trauma. Meanwhile, endovascular injection of embolic agents may lead to residual lesions and an increased risk of recurrence. Therefore, we recommend a one-time combined treatment conducted in a hybrid operating room to achieve complete resection and effectively reduce intraoperative bleeding risk. Despite presenting challenges and requiring high proficiency, we still recommend this type of combined surgery as a suitable therapeutic option for such diseases.

A multidimensional analysis of the impact of obesity on immune checkpoint inhibitor therapy efficacy

BackgroundObesity is a well-known risk factor for developing malignant tumors and promoting tumor cell growth and spread. However, recent studies have shown that obese cancer patients, who typically have a worse prognosis than nonobese cancer patients, show a significant improvement in survival after receiving immune checkpoint inhibitor (ICI) therapy. This phenomenon is known as the “obesity paradox”. However, this phenomenon is influenced by tumor type and sex. Therefore, this study aimed to explore the impact of obesity on immunotherapy efficacy from multiple perspectives, aiming to verify this paradox and provide new scientific evidence on the effect of obesity on ICI efficacy.MethodsThis retrospective study evaluated the data of patients who received ICI therapy between June 2019 and August 2023. Automatic segmentation of skeletal muscle, subcutaneous fat, and visceral fat was performed using Slice-O-Matic software, and the corresponding skeletal muscle index (SMI), subcutaneous fat index (SFI) and visceral fat index (VFI) were calculated. The neutrophil-to-lymphocyte ratio (NLR) was determined by dividing the neutrophil count by the lymphocyte count. Univariate and multivariate Cox regression analyses were used to evaluate the correlation between body mass index (BMI), body composition parameters, and the NLR with overall survival (OS) and progression-free survival (PFS) in obese patients receiving ICI therapy.ResultsWe analyzed 219 patients with a median age of 60 years (IQR 53–69 years; 155 men and 64 women). Obese patients, particularly those with visceral fat accumulation, exhibited extended OS after ICI therapy (log­rank P = 0.027). Cox multivariate analysis revealed that the NLR (HR = 1.036; 95% CI: 0.996 to 1.078; P = 0.002) was independently associated with OS. Patients with a high NLR had worse OS than those with a low NLR.ConclusionsThis study corroborates the veracity of the "obesity paradox" under specific conditions and identifies NLR as an independent prognostic factor, with elevated NLR indicative of a poor prognosis.

Impedance Spectroscopy Measures of Whole and Segmental Skeletal Muscle Quantity Associated With Strength and Power in Collegiate Ice Hockey Players.

Otremba, JR, Heesch, AJ, Morgan, RM, Poolman, MA, Schindler, GD, and Fitzgerald, JS. Bioelectrical impedance spectroscopy measures of whole and segmental skeletal muscle quantity associated with strength and power in collegiate ice hockey players. J Strength Cond Res XX(X): 000-000, 2024-Bioelectrical impedance spectroscopy (BIS) is a promising monitoring tool for body water compartment assessment, particularly intracellular water (ICW), in which acute decreases are associated with increased muscle damage and reduced function, and chronic changes are associated with muscle quantity. Because little is known about the predictive utility of BIS-derived measures in athletes, this study aimed to assess the association between whole-body and segmental compartment water measured by BIS and maximal-intensity exercise performance in athletes. Twenty-five National Collegiate Athletic Association Division I collegiate hockey players completed 2 consecutive testing sessions. Body water and composition were assessed using a SOZO BIS device. Strength and explosive strength were measured on a force platform during the isometric belt squat and squat jump, respectively. Peak power was assessed using the 6-Second Test on a Wattbike. Pearson's r and partial correlation were used to assess relationships. Select body water and composition BIS variables were strong correlates of strength ( r = 0.51-0.63, p < 0.05), moderate correlates of power ( r = 0.41-0.44, p < 0.05), and lacked association with explosive strength. Segmental leg variables tended to be the strongest correlates of strength and power. Body water and traditional lean mass variables expressed similar predictive utility. Intracellular water/extracellular water lacked associated with exercise performance in bivariate and adjusted models. We provide evidence of the criterion validity of whole-body (i.e., ICW, fat-free mass, skeletal muscle mass [SMM]) and segmental (i.e., leg ICW, leg SMM) BIS variables, related to the quantity of SMM, to predict body-size dependent maximal-intensity exercise performance. Practitioners can use this information to determine which variables to track for performance readiness monitoring.

Intestinal Region-Dependent Impact of NFκB-Nrf Crosstalk in Myenteric Neurons and Adjacent Muscle Cells in Type 1 Diabetic Rats.

Type 1 diabetes affects cytokines as potential inducers of NFκB signalling involved in inflammation and neuronal survival. Our goal was to assess the expression of NFκB p65 and its negative regulator, Nrf2, in myenteric neurons and adjacent smooth muscle of different gut segments after chronic hyperglycaemia and immediate insulin treatment. After ten weeks of hyperglycaemia, intestinal samples of control, streptozotocin-induced diabetic and insulin-treated diabetic rats were prepared for fluorescent immunohistochemistry, immunogold electron microscopy, ELISA and qPCR. In the diabetic rats, the proportion of NFκB p65-immunoreactive myenteric neurons decreased significantly in the duodenum and increased in the ileum. The density of NFκB p65-labelling gold particles increased in the ileal but remained unchanged in the duodenal ganglia. Meanwhile, both total and nuclear Nrf2 density increased in the myenteric neurons of the diabetic duodenum. In smooth muscle, NFκB p65 and Nrf2 density increased in the small intestine of diabetic rats. While on the mRNA level, NFκB p65 and Nrf2 were induced, on the protein level, NFκB p65 increased and Nrf2 decreased in muscle/myenteric plexus homogenates. Insulin treatment had protective effects. Our findings reveal a segment-specific NFκB and Nrf expression in myenteric neurons and ganglionic muscular environments, which may contribute to regional neuronal survival and motility disturbances in diabetes.

Accelerated muscle mass estimation from CT images through transfer learning

BackgroundThe cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a certain device often does not work with images from a different device.MethodsIn this study, we propose an efficient learning strategy for deep learning models in medical image segmentation. We aim to overcome the difficulties of segmentation in CT images by training a VNet segmentation model which enables rapid labeling of organs in CT images with the model obtained by transfer learning using a small number of manually labeled images, called SEED images. We established a process for generating SEED images and conducting transfer learning a model. We evaluate the performance of various segmentation models such as vanilla UNet, UNETR, Swin-UNETR and VNet. Furthermore, assuming a scenario that a model is repeatedly trained with CT images collected from multiple devices, in which is catastrophic forgetting often occurs, we examine if the performance of our model degrades.ResultsWe show that transfer learning can train a model that does a good job of segmenting muscles with a small number of images. In addition, it was confirmed that VNet shows better performance when comparing the performance of existing semi-automated segmentation tools and other deep learning networks to muscle and liver segmentation tasks. Additionally, we confirmed that VNet is the most robust model to deal with catastrophic forgetting problems.ConclusionIn the 2D CT image segmentation task, we confirmed that the CNN-based network shows better performance than the existing semi-automatic segmentation tool or latest transformer-based networks.

Effects of capsaicin on esophageal peristalsis in humans using high resolution manometry.

Capsaicin-containing red pepper sauce suspension augments esophageal contraction amplitude on conventional manometry. This study used high-resolution manometry (HRM) to investigate if capsaicin infusion modulates segmental esophageal smooth muscle peristalsis in healthy adults. Sixteen healthy volunteers (mean age 37 years, 14 male) underwent HRM for the evaluation of primary peristalsis and secondary peristalsis using slow and rapid air distensions. Both primary and secondary peristalsis were assessed following infusions of capsaicin-containing red pepper sauce and saline. Capsaicin infusion significantly increased heartburn symptoms compared to saline infusion (p < 0.001), and significantly decreased threshold volumes of secondary peristalsis during rapid air distensions (p = 0.02). The frequency of secondary peristalsis during rapid air distensions was significantly increased by capsaicin infusion (p = 0.03). Neither capsaicin infusion (p = 0.06) nor saline infusion (p = 0.27) altered threshold volume during slow air distensions. Capsaicin infusion significantly increased distal contractile integral (DCI) of primary peristalsis (p = 0.04), particularly in the proximal smooth muscle segment (p = 0.048). It enhanced secondary peristalsis during rapid air distensions (p = 0.003) but not during slow air distension (p = 0.23). Saline infusion significantly increased DCI of secondary peristalsis during rapid air distension (p = 0.01). Augmentation of distension-induced secondary peristalsis can be modulated by activation of capsaicin-sensitive afferents similar to mechanosensitive afferents. Capsaicin-induced augmentation of primary peristalsis isolates to the cholinergic-mediated proximal smooth muscle segment, which warrants study in ineffective esophageal motility to determine therapeutic potential.

Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography

Objective.Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles.Approach.Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated.Main results.Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice similarity coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of -0.84 for the pCT dataset and -0.89 for the sCT dataset.Significance.This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome.

Development and Validation of a Novel Nomogram for Predicting Mechanical Ventilation After Cervical Spinal Cord Injury

ObjectiveTo investigate the risk factors relating to the need for mechanical ventilation (MV) in isolated patients with cervical spinal cord injury (cSCI) and to construct a nomogram prediction model. DesignRetrospective analysis study. SettingNational Spinal Cord Injury Model System Database (NSCID) observation data were initially collected during rehabilitation hospitalization. ParticipantsA total of 5784 patients (N=5784) who had a cSCI were admitted to the NSCID between 2006 and 2021. InterventionsNot applicable. Main Outcome Measure(s)A univariate and multivariate logistic regression analysis was used to identify the independent factors affecting the use of MV in patients with cSCI, and these independent influencing factors were used to develop a nomogram prediction model. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency and the clinical application value of the model, respectively. ResultsIn a series of 5784 included patients, 926 cases (16.0%) were admitted to spinal cord model system inpatient rehabilitation with the need for MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association Impairment Scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM), and neurologic level of injury (NLI) were independent predictors for the use of MV (P<.05). The prediction nomogram of MV usage in patients with cSCI was established based on the above independent predictors. The AUROC of the training set, internal verification set, and external verification set were 0.871 (0.857-0.886), 0.867 (0.843-0.891), and 0.850 (0.824-0.875), respectively. The calibration curve and DCA results showed that the model had good calibration and clinical practicability. ConclusionsThe nomograph prediction model based on sUEM, NLI, associated injury, and AIS can accurately and effectively predict the risk of MV in patients with cSCI, to help clinicians screen high-risk patients and formulate targeted intervention measures.

Evaluation of the reliability of measuring lower back muscles cross-sectional area based on manual segmentation within multi-level MRI images

IntroductionManual segmentation of paraspinal muscle cross-sectional area (CSA) is widely used to assess related health disorders. This study aimed to evaluate the reliability of this segmentation process for each paraspinal region of interest across the three intervertebral levels commonly used for segmentation (L3/4, L4/5, and L5/S1). MethodsAxial-T2-weighted MRI images for 238 patients were divided among five raters (47 ± 1 cases each). To conduct the intra-rater reliability study, the CSA of each paraspinal lumber muscle (psoas major (PM), multifidus (MF), and erector spinae (ES)) and the intervertebral disc (ID) were manually segmented twice on all targeted levels before being assessed for each region per rater. The Inter-rater reliability was determined by comparing the results of different readers who segmented the same dataset. The Intraclass Correlation (ICC) and Coefficient-of-Variation percentage (CV%) were reported for each analysis. ResultsLow intra- and inter-rater variability (CV%<11) was found for each reader in each region and intervertebral level. The inter-rater reliability was excellent (ICC>0.9) for the PM, ID, and ES at L3/4. However, it was very good for MF at all levels and ES at L4/5, L5/S1 (ICC range: 0.82–0.88) affected by the fat-infiltration nature of the ES and MF muscles, their proximity to each other, and their smaller size (correlation between muscle size and ICC = 0.6, P < 0.01). The ID segmentation has the lowest CV (<3 %) and excellent ICC (>0.93). ConclusionManual paraspinal muscle segmentation using axial-T2-weighted MRI is reliable at all commonly segmented intervertebral levels. However, the reliability level can be degraded by the presence of high-fat infiltrate, unclear muscle boundaries, and muscle size. Implications for practiceFollowing consistent guidelines can help improve segmentation results. The IDs can be used as reliable internal references.

Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.

Hip-related pain (HRP) affects young to middle-aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep-learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine-learning segmentations compared with human-generated segmentation. This cross-sectional study included sub-elite/amateur football players (Australian football and soccer) with a greater than 6-month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub-elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine-learning approach was compared with human-performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen-Dice index. When considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). No differences were observed between groups for gluteus maximus (18 265 mm3 [-21 209, 50 782]; p = 0.419) or minimus (3893 mm3 [-2209, 9996]; p = 0.21). The CNN was trained for 30 000 iterations and assessed its accuracy and reliability on an independent testing dataset, achieving high segmentation accuracy (mean Sorenson-Dice index >0.900) and excellent muscle volume and MFI reliability (ICC2,1 > 0.900). The CNN outperformed manual raters, who had slightly lower interrater accuracy (Sorensen-Dice index >0.800) and reliability (ICC2,1 > 0.800). The increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.

An Artificial Intelligence-Based Tool for Enhancing Pectoral Muscle Segmentation in Mammograms: Addressing Class Imbalance and Validation Challenges in Automated Breast Cancer Diagnosis.

Breast cancer remains a major health concern worldwide, requiring the advancement of early detection methods to improve prognosis and treatment outcomes. In this sense, mammography is regarded as the gold standard in breast cancer screening and early detection. However, in a scenario where extensive analysis is required, a large set of mammograms conducted by radiologists may carry out false negative or false positive diagnoses. Therefore, artificial intelligence has emerged in recent years as a method for enhancing timing in breast cancer diagnosis. Nonetheless, preprocessing stages are required to prepare the mammography dataset to enhance learning models to correctly identify breast anomalies. In this paper, we introduce a novel method employing convolutional neural networks (CNNs) to segment the pectoral muscle in 1288 mediolateral oblique mammograms (MLOs), thereby addressing class imbalance and overfitting between classes, and dataset augmentation based on translation, rotation, and scale transformation. The effectiveness of the model was assessed through a confusion matrix and performance metrics, highlighting an average Dice coefficient of 0.98 and a Jaccard index of 0.96. The outcomes demonstrate the model capability to accurately identify three classes: pectoral muscle, breast, and background. This study emphasizes the importance of tackling class imbalance problems and augmenting data for the training of models for reliable early breast cancer detection.

A rare case of total hip replacement in an 11-year-old female: rehabilitation goals and challenges - a case report

This case report describes a unique pediatric patient, the first and youngest in India, who underwent total hip replacement (THR) for right ankylosed hip. Patient presented with preoperative musculoskeletal deformities, posing challenges for postoperative rehabilitation. This 11-year-old female presented with non-traumatic insidious right hip pain, limping, right hip flexion and abduction deformity. Patient was diagnosed with idiopathic chondrolysis of the hip through exclusion and clinico-radiological findings. Patient underwent surgery for ceramic-on-ceramic THR. Fusion of triradiate cartilage and epiphysis and Risser’s skeletal maturity staging were confirmed. Postoperative examination revealed right hip flexion and abduction deformity resulting in pelvic obliquity, which led to limb length discrepancy and functional scoliosis. There was increased anterior pelvic tilt, altering the gait pattern. Adhering to THR restrictions, rehabilitation strategies were tailored to restore normal pelvic orientation and address deformities through manual stretching of right hip abductor, tensor fascia latae, iliopsoas and rectus femoris. A left-sided shoe raise was also given. Lumbar segmental muscle activation, pelvic floor muscle training and gait training were crucial parts of the rehabilitation protocol. At ten-week, prodigious outcomes were achieved in the form of complete pain relief, complete correction of abduction and flexion deformity, normal pelvic alignment in the frontal plane and independent, stable full weight-bearing ambulation after two years of disabling ordeal suffering. However, minimal compensation of the anterior pelvic tilt persisted. Rehabilitation of this challenging patient with multiple deformities achieved excellent outcomes at ten-week post-rehabilitation. Thus, a tailored meticulous rehabilitation program can significantly improve quality of life.

Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images

The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.

Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia

PurposeTo compare the performance of MRI-based Gaussian mixture model (GMM), K-means clustering, and Otsu unsupervised algorithms in predicting sarcopenia and to develop a combined model by integrating clinical indicators. MethodsRetrospective analysis was conducted on clinical and lumbar MRI data from 118 patients diagnosed with sarcopenia and 222 patients without the sarcopenia. All patients were randomly divided into training and validation groups in a 7:3 ratio. Regions of interest (ROI), specifically the paravertebral muscles at the L3/4 intervertebral disc level, were delineated on axial T2-weighted images (T2WI). The Gaussian mixture model (GMM), K-means clustering, and Otsu’s thresholding algorithms were employed to automatically segment muscle and adipose tissues at both the cohort and case levels. Subsequently, the mean signal intensity, volumes, and percentages of these tissues were calculated and compared. Logistic regression analyses were conducted to construct models and identify independent predictors of sarcopenia. An combined model was developed by combining the optimal magnetic resonance imaging (MRI) model and clinical predictors. The performance of the constructed model was assessed using receiver operating characteristic (ROC) curve analysis. ResultsAge, BMI, and serum albumin were identified as independent clinical predictors of sarcopenia. The cohort-level GMM demonstrated the best predictive performance both in the training group (AUC=0.840) and validation group (AUC=0.800), while the predictive performance of the other models was lower than that of the clinical model both in the training and validation groups. After combining the cohort-level GMM with the independent clinical predictors, the AUC of the training and validation groups increased to 0.871 and 0.867, respectively. ConclusionThe cohort-level GMM shows potential in predicting sarcopenia, and the incorporation of independent clinical predictors further increased the performance.

External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal computed tomography images.

Body composition assessment using computed tomography (CT) images at the L3-level is increasingly applied in cancer research. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images. Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3,187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2,535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) Dice Similarity (DS) and Lin's Concordance Correlation Coefficient. There was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (interquartile range, IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 hounsfield units (0.5%), SM: 1.26 cm2.m-2 (2.8%), VAT: -1.02 cm2.m-2 (1.7%), and SAT: 3.24 cm2.m-2 (4.6%). A robustly-performing and independently externally validated DLNN for automated body composition analysis was developed. CT-based body composition analysis is highly prognostic for long-term overall survival in oncology. This DLNN was succesfully trained and externally validated on several large patient cohorts and will therefore enable large scale population studies and implementation of body composition analysis into clinical practice.

Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging.

Lumbar disc herniation (LDH) is a prevalent cause of low back pain. LDH patients commonly experience paraspinal muscle atrophy and fatty infiltration (FI), which further exacerbates the symptoms of low back pain. Magnetic resonance imaging (MRI) is crucial for assessing paraspinal muscle condition. Our study aims to develop a dual-model for automated muscle segmentation and FI annotation on MRI, assisting clinicians evaluate LDH conditions comprehensively. The study retrospectively collected data diagnosed with LDH from December 2020 to May 2022. The dataset was split into a 7:3 ratio for training and testing, with an external test set prepared to validate model generalizability. The model's performance was evaluated using average precision (AP), recall and F1 score. The consistency was assessed using the Dice similarity coefficient (DSC) and Cohen's Kappa. The mean absolute percentage error (MAPE) was calculated to assess the error of the model measurements of relative cross-sectional area (rCSA) and FI. Calculate the MAPE of FI measured by threshold algorithms to compare with the model. A total of 417 patients being evaluated, comprising 216 males and 201 females, with a mean age of 49 ± 15 years. In the internal test set, the muscle segmentation model achieved an overall DSC of 0.92 ± 0.10, recall of 92.60%, and AP of 0.98. The fat annotation model attained a recall of 91.30%, F1 Score of 0.82, and Cohen's Kappa of 0.76. However, there was a decrease on the external test set. For rCSA measurements, except for longissimus (10.89%), the MAPE of other muscles was less than 10%. When comparing the errors of FI for each paraspinal muscle, the MAPE of the model was lower than that of the threshold algorithm. The models demonstrate outstanding performance, with lower error in FI measurement compared to thresholding algorithms.