The success of deep convolutional neural networks (NNs) on image classification and recognition tasks has led to new applications in very diversified contexts, including the field of medical imaging. In this paper, we investigate and propose NN architectures for automated multiclass segmentation of anatomical organs in chest radiographs (CXRs), namely for lungs, clavicles, and heart. We address several open challenges including model overfitting, reducing number of parameters, and handling of severely imbalanced data in CXR by fusing recent concepts in convolutional networks and adapting them to the segmentation problem task in CXR. We demonstrate that our architecture combining delayed subsampling, exponential linear units, highly restrictive regularization, and a large number of high-resolution low-level abstract features outperforms state-of-the-art methods on all considered organs, as well as the human observer on lungs and heart. The models use a multiclass configuration with three target classes and are trained and tested on the publicly available Japanese Society of Radiological Technology database, consisting of 247 X-ray images the ground-truth masks for which are available in the segmentation in CXR database. Our best performing model, trained with the loss function based on the Dice coefficient, reached mean Jaccard overlap scores of 95% for lungs, 86.8% for clavicles, and 88.2% for heart. This architecture outperformed the human observer results for lungs and heart.

No abstract

Fig. 1: Neural projections in the brain of the fruit fly visualized using the BrainGazer system. Abstract-Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly Drosophila melanogaster, have proven to be an important tool in this research. Large databases of transgenic specimens are being built and need to be analyzed to establish models of neural information processing. In this paper we present an approach for the exploration and analysis of neural circuits based on such a database. We have designed and implemented BrainGazer, a system which integrates visualization techniques for volume data acquired through confocal microscopy as well as annotated anatomical structures with an intuitive approach for accessing the available information. We focus on the ability to visually query the data based on semantic as well as spatial relationships. Additionally, we present visualization techniques for the concurrent depiction of neurobiological volume data and geometric objects which aim to reduce visual clutter. The described system is the result of an ongoing interdisciplinary collaboration between neurobiologists and visualization researchers.

Quality of segmentations obtained by 3D Active Appearance Models (AAMs) crucially depends on underlying training data. MRI heart data, however, often come noisy, incomplete, with respiratoryinduced motion, and do not fulfill necessary requirements for building an AAM. Moreover, AAMs are known to fail when attempting to model local variations. Inspired by the recent work on split models [1] we propose an alternative to the methods based on pure 3D AAM segmentation. We interconnect a set of 2D AAMs by a 3D shape model. We show that our approach is able to cope with imperfect data and improves segmentations by 11% on average compared to 3D AAMs.

No abstract

No abstract

No abstract

Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.