1,721,008 research outputs found
End-to-end deep learning and sensor fusion for non-invasive BP monitoring using multivariate physiological signals
No full textComparison of supervised and unsupervised approaches for the generation of synthetic ct from cone-beam ct
Full text linkDue to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation
Pair-wise vs group-wise registration in statistical shape model construction: representation of physiological and pathological variability of bony surface morphology
No full textStatistical shape models (SSM) of bony surfaces have been widely proposed in orthopedics, especially for anatomical bone modeling, joint kinematic analysis, staging of morphological abnormality, and pre- and intra-operative shape reconstruction. In the SSM computation, reference shape selection, shape registration and point correspondence computation are fundamental aspects determining the quality (generality, specificity and compactness) of the SSM. Such procedures can be made critical by the presence of large morphological dissimilarities within the surfaces, not only because of anthropometrical variability but also mainly due to pathological abnormalities. In this work, we proposed a SW pipeline for SSM construction based on pair-wise (PW) shape registration, which requires the a-priori selection of the reference shape, and on a custom iterative point correspondence algorithm. We addressed large morphological deformations in five different bony surface sets, namely proximal femur, distal femur, patella, proximal fibula and proximal tibia, extracted from a retrospective patient dataset. The technique was compared to a method from the literature, based on group-wise (GW) shape registration. As a main finding, the proposed technique provided generalization and specificity median errors, for all the five bony regions, lower than 2mm. The comparative analysis provided basically similar results. Particularly, for the distal femur that was the shape affected by the largest pathological deformations, the differences in generalization, specificity and compactness were lower than 0.5mm, 0.5mm, and 1%, respectively. We can argue the proposed pipeline, along with the robust correspondence algorithm, is able to compute high-quality SSM of bony shapes, even affected by large morphological variability
Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model
Full text linkStatistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called “modes of variation” (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p = 0.02) in correspondence to IER and MoV #5 in correspondence to HKA (p = 0.0001), FVV (p = 0.001), and TS (p = 0.02). We can argue that the SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict potential misalignment in the knee for a new patient by processing the bone shapes, removing the need for measuring clinical landmarks as the rotation centers and mechanical axes
Representative 3D shape of the distal femur, modes of variation and relationship with abnormality of the trochlear region
No full textThe anatomy of the distal femur has a predominant influence on the mechanics of both patello- and tibio-femoral joints. Especially, the morphological degeneration of the trochlear region dramatically affects the overall knee biomechanics and, from a clinical point of view, the staging of such a degeneration is fundamental to tailor the optimal therapeutic solution. The description of morphological variability and pathological inter-subject differences of the trochlea can be achieved by means of statistical shape modeling of a set of three-dimensional surfaces. This representation encodes information, spread into the dataset, in terms of modes of variations that model global, regional and even local morphological features. In view of that, the aim of this study was to develop a statistical shape model of the distal femur to capture the variability of the trochlear region into specific modes of variation and to study the interplay between the variation of the trochlea and the condylar regions. Using CT scans of patients affected by different levels of abnormality of the trochlear region, the distal femur geometries were co-registered to a reference shape using the pair-wise correspondence approach and principal component analysis provided the key modes of variation (MoVs). Apart from the first two MoVs, which described the global magnitude of the femur and the shaft length, the main following ones showed high correlation with sulcus depth (r(2) = 0.70), sulcus angle (r(2) = 0.70), lateral trochlear inclination (r(2) = 0.66), and height of the two condylar facets in the anterior direction (r(2) = 0.66), whose abnormal variations are typical signs of trochlear degeneration. High interplay between trochlear abnormalities and notch width (r(2) = 0.71), lateral condylar size (r(2) = 0.67), and medial condylar size (r(2) = 0.99) was found. Interestingly, the model predicted morphological associations not included in the training dataset, nonetheless difficult to demonstrate physiologically. Interestingly from a biomechanical point of view, the distribution of some MoVs was found statistically different across the patients featuring physiological and pathological ranges of hip-knee-ankle alignment, femoral internal-external rotation and tibial slope. However, no linear correlation was found between the angular indexes and such MoVs. As a result, we can assert that statistical modeling of the distal femur are to date an effective way to visualize and quantify abnormalities of the trochlear regions supporting the introduction of advanced analysis, diagnostic and treatment support tools to elucidate physiologic and pathological variability in the morphology, to drive the staging and assist the selection of the optimal treatment option tailored to the patient. (C) 2019 Elsevier Ltd. All rights reserved
Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality
Full text linkAccurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications
Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning
No full textGoal: Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. Methods: xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). Results: Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. Conclusions: this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty
Humeral head necrosis associated to shaft non-union with massive bone loss: A case report
Full text linkHumeral non-union is a rare complication in shaft fractures, as well as humeral head necrosis is a possible complication in fracture involving the proximal third especially in four-part fractures. The presence of head osteonecrosis and diaphyseal non-union in the same arm represents a formidable challenge for an orthopaedic surgeon. We could not find any similar report in the literature dealing with this issue thus far. We present a case of a 65 years old woman referred to our hospital being affected by an atrophic humeral diaphyseal non-union with a massive bone loss (>10cm) associated to a humeral head osteonecrosis following a previous surgical procedures with a clear loosening of the hardware. At our institution, she was treated with hardware removal and insertion of a diaphyseal antibiotic spacer with Gentamycin for 2 months suspecting an active septic process at the union site despite negative cultural exams. Finally, she was treated with a cemented modular humeral megaprosthesis. At 20 months follow up, the patient, despite a reduced shoulder range of motion, referred to a pain-free recovery to an almost normal lifestyle, including car driving with no major disturbances. This case suggests that, in extreme selected cases following several failed treatments, megapros-thesis can represent a viable solution, especially in huge bone loss associated to joint degeneration, to ensure an acceptable return to a normal lifestyle. (www.actabiomedica.it)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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