Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the nex...

https://www.tandfonline.com/doi/pdf/10.1080/17453674.2017.1344459

Artificial intelligence for analyzing orthopedic trauma radiographs

A display apparatus includes an image processor which processes an image to display an image content; a display unit which displays thereon an image content that is processed by the image processor; and a controller which displays a user interface (UI) menu including a plurality of UI items to search the image content, as one of a two-dimensional (2D) layout by which the plurality of UI items are arranged in a 2D manner, and a three-dimensional (3D) layout by which the plurality of UI items are arranged in a 3D manner , and changes displaying the one of the layouts to display the other of the layouts according to a user's command while maintaining a continuity of the arrangement of the plurality of UI items. Accordingly, search for an image content can be efficiently performed by using a UI menu.

Display apparatus and control method thereof

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.

/pdf/deep-learning-in-radiology-an-overview-of-the-concepts-and-a-35hnf1jtsa.pdf

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review the broad range of utilized deep learning algorithms. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.

Deep learning in radiology: an overview of the concepts and a survey of the state of the art.

To compare performances in diagnosing intertrochanteric hip fractures from proximal femoral radiographs between a convolutional neural network and orthopedic surgeons. In total, 1773 patients were enrolled in this study. Hip plain radiographs from these patients were cropped to display only proximal fractured and non-fractured femurs. Images showing pseudarthrosis after femoral neck fracture and those showing artificial objects were excluded. This yielded a total of 3346 hip images (1773 fractured and 1573 non-fractured hip images) that were used to compare performances between the convolutional neural network and five orthopedic surgeons. The convolutional neural network and orthopedic surgeons had accuracies of 95.5% (95% CI = 93.1–97.6) and 92.2% (95% CI = 89.2–94.9), sensitivities of 93.9% (95% CI = 90.1–97.1) and 88.3% (95% CI = 83.3–92.8), and specificities of 97.4% (95% CI = 94.5–99.4) and 96.8% (95% CI = 95.1–98.4), respectively. The performance of the convolutional neural network exceeded that of orthopedic surgeons in detecting intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. The convolutional neural network has a significant potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.

Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network

Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine. To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense. 12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist. The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies ‘Evidence Hotspots’ that are the voxels that most contribute to the degradation scores. Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts. Level of Evidence: Level 3.

/pdf/issls-prize-in-bioengineering-science-2017-automation-of-3todndjmh1.pdf

ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

We describe a method to automatically detect and label the vertebrae in human lumbar spine MRI scans. Our contribution is to show that marrying two strong algorithms (the DPM object detector of Felzenszwalb et al. [1], and inference using dynamic programming on chains) together with appropriate modelling, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance. The training of the algorithm is principled, and heuristics are not required. The method is evaluated quantitatively on a dataset of 371 MRI scans, and it is shown that the method copes with pathologies such as scoliosis, joined vertebrae, deformed vertebrae and disks, and imaging artifacts. We also demonstrate that the same method is applicable (without retraining) to CT scans.

/pdf/vertebrae-detection-and-labelling-in-lumbar-mr-images-22ebsg955q.pdf

Vertebrae Detection and Labelling in Lumbar MR Images

A method and apparatus is provided for reducing colour shift in relation to viewing angle in an LCD. The method includes receiving a plurality of pixel data constituting an image, each pixel data including a plurality of sub-pixel colour components having respective data values; for each of the pixel data, comparing the sub-pixel colour component data values included therein; and based on the comparison, modifying the sub-pixel colour component data values included in the pixel data with respect to two or more of the plurality of sub-pixel colour components to reduce colour shift when displayed on the LCD.

Adaptive image processing method and apparatus for reduced colour shift in LCDs

This paper describes a fully automatic system for obtaining the standard Pfirrmann degeneration grading of individual intervertebral spinal discs in T2 MRI scans. It involves detecting and labeling all the vertebrae in the scan and then learning a regression from the disc region to the grading. In developing the regression function we investigate a spectrum of support regions which involve differing degrees of segmentation of the scan: our intention is to ascertain to what extent segmentation is necessary or detrimental in obtaining robust and accurate measurements. The methods are assessed on a heterogeneous clinical dataset containing 1,710 Pfirrmann-graded discs, from 285 symptomatic back pain patients. We are able to predict the grade to \(\pm 1\) precision at 85.8 % accuracy. Our novel method proposes new image features that outperform previous features and utilizes techniques to improve robustness to MR imaging variations.

Automated Radiological Grading of Spinal MRI

This paper describes an automatic system for intervertebral discs (IVDs) localization and segmentation in three-dimensional magnetic resonance imaging scans. The system builds upon the localization and segmentation system first introduced by Lootus et al. with several improvements to the localization step. The system was trained on T1 and T2 scans of 341 patients obtained from various sources. The proposed system achieved a mean localization error of \(1.1\,{\pm }\,0.6\) mm and a mean Dice overlap coefficient of \(84.0\,{\pm }\,1.5\,\%\) on the 15 training data of the challenge on IVD localization and segmentation at the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015.

Meelis Lootus

Papers

ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

Vertebrae Detection and Labelling in Lumbar MR Images

Adaptive image processing method and apparatus for reduced colour shift in LCDs

Automated Radiological Grading of Spinal MRI

Automatic Intervertebral Discs Localization and Segmentation: A Vertebral Approach