Radiographic software measurement analysis in adult scoliosis.
To assess the accuracy as well as the intra- and interobserver reliability of measuring different indices on preoperative adult ...scoliosis radiographs using a novel measurement software that includes a calibration procedure and semiautomatic features to facilitate the measurement process.
Scoliosis requires a careful radiographic evaluation to assess the deformity. Manual and computer radiographic process measures have been studied extensively to determine the reliability and reproducibility in adolescent idiopathic scoliosis. Most studies rely on comparing given measurements, which are repeated by the same user or by an expert user. A given measure with a small intra- or interobserver error might be deemed as good repeatability, but all measurements might not be truly accurate because the ground-truth value is often unknown. Thorough accuracy assessment of radiographic measures is necessary to assess scoliotic deformities, compare these measures at different stages or to permit valid multicenter studies.
Thirty-four sets of adult scoliosis digital radiographs were measured two times by three independent observers using a novel radiographic measurement software that includes semiautomatic features to facilitate the measurement process. Twenty different measures taken from the Spinal Deformity Study Group radiographic measurement manual were performed on the coronal and sagittal images. Intra- and intermeasurer reliability for each measure was assessed. The accuracy of the measurement software was also assessed using a physical spine model in six different scoliotic configurations as a true reference.
The majority of the measures demonstrated good to excellent intra- and intermeasurer reliability, except for sacral obliquity. The standard variation of all the measures was very small: ≤ 4.2° for Cobb angles, ≤ 4.2° for the kyphosis, ≤ 5.7° for the lordosis, ≤ 3.9° for the pelvic angles, and ≤5.3° for the sacral angles. The variability in the linear measurements (distances) was <4 mm. The variance of the measures was 1.7 and 2.6 times greater, respectively, for the angular and linear measures between the inter- and intrameasurer reliability. The image quality positively influenced the intermeasurer reliability especially for the proximal thoracic Cobb angle, T10-L2 lordosis, sacral slope and L5 seating. The accuracy study revealed that on average the difference in the angular measures was < 2° for the Cobb angles, and < 4° for the other angles, except T2-T12 kyphosis (5.3°). The linear measures were all <3.5 mm difference on average.
The majority of the measures, which were analyzed in this study demonstrated good to excellent reliability and accuracy. The novel semiautomatic measurement software can be recommended for use for clinical, research or multicenter study purposes.
Abstract Objective. Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an ...unsolved problem since this imaging modality does not provide information about the density of electrons. Approach. We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network. Results. The proposed model achieves a mean absolute error (MAE) of 18.76 ± 5.167 in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of 0.95 ± 0.09 and a Frechet inception distance (FID) of 145.60 ± 8.38 . The model yields a MAE of 26.83 ± 8.27 to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of 0.73 ± 0.06 and a FID distance equal to 122.58 ± 7.55 . The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of 27.89 ± 2.22 and 26.08 ± 2.95 to synthesize MRI from CT input. Significance. The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.
The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks ...standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88-20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215-0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55-19.58 vs 21.00%; 95% CI: 20.20-21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37-8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.
With the emergence of online MRI radiotherapy treatments, MR-based workflows have increased in importance in the clinical workflow. However proper dose planning still requires CT images to calculate ...dose attenuation due to bony structures. In this paper, we present a novel deep image synthesis model that generates in an unsupervised manner CT images from diagnostic MRI for radiotherapy planning. The proposed model based on a generative adversarial network (GAN) consists of learning a new invariant representation to generate synthetic CT (sCT) images based on high frequency and appearance patterns. This new representation encodes each convolutional feature map of the convolutional GAN discriminator, leading the training of the proposed model to be particularly robust in terms of image synthesis quality. Our model includes an analysis of common histogram features in the training process, thus reinforcing the generator such that the output sCT image exhibits a histogram matching that of the ground-truth CT. This CT-matched histogram is embedded then in a multi-resolution framework by assessing the evaluation over all layers of the discriminator network, which then allows the model to robustly classify the output synthetic image. Experiments were conducted on head and neck images of 56 cancer patients with a wide range of shape sizes and spatial image resolutions. The obtained results confirm the efficiency of the proposed model compared to other generative models, where the mean absolute error yielded by our model was 26.44(0.62), with a Hounsfield unit error of 45.3(1.87), and an overall Dice coefficient of 0.74(0.05), demonstrating the potential of the synthesis model for radiotherapy planning applications.
In this paper, we introduce a fully unsupervised approach for the synthesis of CT images of the lumbar spine, used for image-guided surgical procedures, from a T2-weighted MRI acquired for diagnostic ...purposes. Our approach makes use of a trainable pre-processing pipeline using a low-capacity fully convolutional network, to normalize the input MRI data, in cascade with FC-ResNets, to segment the vertebral bodies and pedicles. A pseudo-3D Cycle GAN architecture is proposed to include neighboring slices in the synthesis process, along with a cyclic loss function ensuring consistency between MRI and CT synthesis. Clinical experiments were performed on the SpineWeb dataset, totalling 18 patients with both MRI and CT. Quantitative comparison to expert CT segmentations yields an average Dice score of 83 ± 1.6 on synthetic CTs, while a comparison to CT annotations yielded a landmark localization error of 2.2 ± 1.4mm. Intensity distributions and mean absolute errors in Hounsfield units also show promising results, illustrating the strong potential and versatility of the pipeline by achieving clinically viable CT scans which can be used for surgical guidance.
Surgical guidance applications using Raman spectroscopy are being developed at a rapid pace in oncology to ensure safe and complete tumor resection during surgery. Clinical translation of these ...approaches relies on the acquisition of large spectral and histopathological data sets to train classification models. Data calibration must ensure compatibility across Raman systems and predictive model transferability to allow multi‐centric studies to be conducted. This paper addresses issues relating to Raman measurement standardization by first comparing Raman spectral measurements made on an optical phantom and acquired with nine distinct point probe systems and one wide‐field imaging instrument. Data standardization method led to normalized root‐mean‐square deviations between instruments of 2%. A classification model discriminating between white and gray matter was trained with one point probe system. When used to classify independent data sets acquired with the other systems, model predictions led to >95% accuracy, preliminarily demonstrating model transferability across different biomedical Raman spectroscopy instruments.
Three hand‐held probes, one portable wide‐field imaging instrument (labeled WF), three spectrometers and one 785 nm laser source were combined to build a total of 10 different Raman spectroscopy systems.
Deep Learning: A Primer for Radiologists Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene ...
Radiographics,
2017 Nov-Dec, Letnik:
37, Številka:
7
Journal Article
Recenzirano
Odprti dostop
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and ...playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging.
RSNA, 2017.
Highlights • A novel groupwise shape analysis approach is proposed to detect regional morphological alterations in sub-cortical structures between two study groups, e.g., healthy and pathological ...subjects. • The proposed framework applies spectral matching in order to find point-to point correspondences across all surfaces. • The proposed framework is applied on the clinical application of Alzheimer's Disease for detecting abnormal sub-cortical shape variations.
We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes. We build the model from two fully convolutional networks, connected in tandem and ...trained together end-to-end. We evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge, attaining competitive liver and liver lesion detection and segmentation scores across a wide range of metrics. Unlike other top performing methods, our model output post-processing is trivial, we do not use data external to the challenge, and we propose a simple single-stage model that is trained end-to-end. However, our method nearly matches the top lesion segmentation performance and achieves the second highest precision for lesion detection while maintaining high recall.
Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, ...which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g., T 1 -, T 2 - and T 2 *-weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter- and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 ± 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts.
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