Publications by authors named "Borotikar B"

Recent studies in medical image segmentation involve new automatic approaches where active learning models are useful with less training samples. Presence of homogenous and heterogenous intensities for a single anatomical structure in pediatric musculoskeletal MR images affects the accuracy in terms of segmentation and classification of labels. This study addresses the homogeneity in intensity issues and introduces a new pre-training pipeline framework of Multi-level Otsu thresholding image as separate channel for 3D UNet model training.

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Introduction: Instant messaging applications (MAs) represent a major component of modern telecommunications for data transmission. During overseas deployments, military doctors increasingly rely on MAs due to their availability and the urgent need to obtain advice from specialists for optimal patient management. In this study, we aimed to describe and analyze the context and usage characteristics of these MAs for transmitting medical data by military general practitioners (GPs) during overseas missions.

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In model-based medical image analysis, three relevant features are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical properties. Often, these features are modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. However, analysing articulated objects in an image using independent single object models may lead to large uncertainties and impingement, especially around organ boundaries.

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Cerebral palsy, a common physical disability in childhood, often causes abnormal patterns of movement and posture. To better understand the pathology and improve rehabilitation of patients, a comprehensive bone shape analysis approach is proposed in this article. First, a group analysis is performed on a clinical MRI dataset using two state-of-the-art shape analysis methods: ShapeWorks and a voxel-based method relying on Advanced Normalization Tools (ANTs) registration.

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Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images.

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Image-based diagnosis routinely depends on more that one image modality for exploiting the complementary information they provide. However, it is not always possible to obtain images from a secondary modality for several reasons such as cost, degree of invasiveness and non-availability of scanners. Three-dimensional (3D) morphable models have made a significant contribution to the field of medical imaging for feature-based analysis.

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Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations. In the medical image processing pipeline, semantic segmentation using deep learning algorithms enables an automatic generation of patient-specific three-dimensional anatomical models which are crucial for morphological evaluation. However, the scarcity of pediatric imaging resources may result in reduced accuracy and generalization performance of individual deep segmentation models.

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Background: Real-time sequences allow functional evaluation of various joint structures during a continuous motion and help understand the pathomechanics of underlying musculoskeletal diseases.

Purpose: To assess and compare the image quality of the two most frequently used real-time sequences for joint dynamic magnetic resonance imaging (MRI), acquired during finger and ankle joint motion.

Material And Methods: A real-time dynamic acquisition protocol, including radiofrequency (RF)-spoiled and balanced steady-state free precession (bSSFP) sequences, optimized for temporal resolution with similar spatial resolution, was performed using a 3.

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The goal of this study was to assess and compare the precision and accuracy of nine and seven methods usually used in Computer Assisted Orthopedic Surgery (CAOS) to estimate respectively the Knee Center (KC) and the Frontal Plane (FP) for the determination of the HKA angle (HKAA). An in-vitro experiment has been realized on thirteen cadaveric lower limbs. A CAOS software application was developed and allowed the computation of the HKAA according to these nine KC and seven FP methods.

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Objective: Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature.

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Current lower limb musculoskeletal (MSK) models focus on sagittal plane kinematics. However, abnormal gait is typically associated with sagittal plane motions crossing into other planes, limiting the use of current MSK models. The purpose of this study was twofold, first, to extend the capability of a full-body MSK model from the literature to include frontal knee plane kinematics during healthy gait, and second, to propose and implement a realistic muscle discretization technique.

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The risk for ischiofemoral impingement has been mainly related to a reduced ischiofemoral distance and morphological variance of the femur. From an evolutionary perspective, however, there are strong arguments that the condition may also be related to sexual dimorphism of the pelvis. We, therefore, investigated the impact of gender-specific differences in anatomy of the ischiofemoral space on the ischiofemoral clearance, during static and dynamic conditions A random sampling Monte-Carlo experiment was performed to investigate ischiofemoral clearance during stance and gait in a large ( = 40 000) virtual study population, while using gender-specific kinematics.

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Introduction: Ulnar tunnel syndrome at the elbow is a common pathology. The ultrasound cross-sectional area is a well-known metric widely accepted in radiology for the description of nerve entrapment. However, the pathological cut-off value remains challenging.

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Dynamic magnetic resonance imaging (MRI) is a non-invasive method that can be used to increase the understanding of the pathomechanics of joints. Various types of real-time gradient echo sequences used for dynamic MRI acquisition of joints include balanced steady-state free precession sequence, radiofrequency-spoiled sequence, and ultra-fast gradient echo sequence. Due to their short repetition time and echo time, these sequences provide high temporal resolution, a good signal-to-noise ratio and spatial resolution, and soft tissue contrast.

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Article Synopsis
  • Patient-specific biomechanical simulations of joints need accurate bone anatomy reconstruction from medical images, with joint shapes impacting biomechanics.
  • Statistical shape models (SSMs) capture the natural variations of biological shapes, but they lack kinematic information detailing the relationship between interacting objects.
  • The proposed framework combines statistical shape and kinematics models (SSKMs) to analyze this relationship, demonstrated through 3D imaging of cadaveric shoulder joints, and aims to further understand joint biomechanics in future studies.
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Dynamic MRI has made it possible to non-invasively capture the moving human joints in vivo. Real-time Fast Field Echo (FFE) sequences have the potential to reduce the effect of motion artifacts by acquiring the image data within a few milliseconds. However, the short acquisition times affect the temporal resolution of the acquired sequences.

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Spatio-temporal evolution of joint space width (JSW) during motion is of great importance to help with making early treatment plans for degenerative joint diseases like osteoarthritis (OA). These diseases can affect people of all ages leading to an acceleration of joint degeneration and to limitations in the activities of daily living. However, only a few studies have attempted to quantify the JSW from moving joints.

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Objective: To illustrate (a) whether a statistical shape model (SSM) augmented with anatomical landmark set(s) performs better fitting and provides improved clinical relevance over non-augmented SSM and (b) which anatomical landmark set provides the best augmentation strategy for predicting the glenoid region of the scapula.

Methods: Scapula SSM was built using 27 dry bone CT scans and augmented with three anatomical landmark sets (16 landmarks each) resulting in three augmented SSMs (aSSM, aSSM, aSSM). The non-augmented and three augmented SSMs were then used in a non-rigid registration (regression) algorithm to fit to six external scapular shapes.

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Prediction of complete and premorbid scapular anatomy is an important aspect of successful shoulder arthroplasty surgeries to treat glenohumeral arthritis and which remains elusive in the current literature. We proposed to build a statistical shape model (SSM) of the scapula and use it to build a framework to predict a complete scapular shape from virtually created scapular bone defects. The bone defects were synthetically created to imitate bone loss in the glenoid region and missing bony part in inferior and superior scapular regions.

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Changes in lower-extremity bone morphology are potential mid- to long-term secondary consequences of cerebral palsy (CP), affecting activity. Little is known about the 3-D morphology of lower-extremity bones in children with CP and the association with gait deviations. The main aim of this study was to describe and compare 3-D lower-extremity bone morphology in ambulant children with unilateral or bilateral CP.

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The purpose of this study was to determine whether optimal epiphyseal screw length could be predicted with reference to a given diaphyseal screw length when fixating a plate to the anterior surface of the distal radius. Computerized tomography scans of 40 wrists of 28 men and 12 women were semi-automatically segmented. A virtual anterior plate model was fixed to the distal radius.

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The gleno-humeral (GH) rotation centre is typically estimated using predictive or functional methods, however these methods may lead to location errors. This study aimed at determining a location error threshold above which statistically significant changes in the values of kinematic and kinetic GH parameters occur. The secondary aims were to quantify the effects of the direction of mislocation (X, Y or Z axis) of the GH rotation centre on GH kinematic and kinetic parameters.

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In this paper, we propose a method for non-invasively measuring three-dimensional in vivo kinematics of the ankle joint from a dynamic MRI acquisition of a single range-of-motion cycle. The proposed approach relies on an intensity-based registration method to estimate motion from multi-plane dynamic MRI data. Our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the joint.

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