Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential regionof-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People’s Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients

Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Computer-aided diagnosis in chest radiography

Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an automatic system for detecting regions with abnormal texture, that is applied to a database of images from a tuberculosis screening program

A computer aided diagnosis system for lung nodules detection in postero anterior chest radiographs

This thesis describes a Computer Aided System aimed at lung nodules detection. The fully automatized method developed to search for nodules is composed by four steps. They are the segmentation of the lung field, the enhancement of the image, the extraction of the candidate regions, and the selection between them of the regions with the highest chance to be True Positives. The steps of segmentation, enhancement and candidates extraction are based on multi-scale analysis. The common assumption underlying their development is that the signal representing the details to be detected by each of them (lung borders or nodule regions) is composed by a mixture of more simple signals belonging to different scales and level of details. The last step of candidate region classification is the most complicate; its 8 task is to discern among a high number of candidate regions, the few True Positives. To this aim several features and different classifiers have been investigated. In Chapter 1 the segmentation algorithm is described; the algorithm has been tested on the images of two different databases, the JSRT and the Niguarda database, both described in the next section, for a total of 409 images. We compared the results obtained with another method presented in the literature and described by Ginneken, in [85], as the one obtaining the best performance at the state of the art; it has been tested on the same images of the JSRT database. No errors have been detected in the results obtained by our method, meanwhile the one previously mentioned produced an overall number of error equal to 50. Also the results obtained on the images of the Niguarda database confirmed the efficacy of the system realized, allowing us to say that this is the best method presented so far in the literature. This sentence is based also on the fact that this is the only system tested on such an amount of images, and they are belonging to two different databases. Chapter 2 is aimed at the description of the multi-scale enhancement and the extraction methods. The enhancement allows to produce an image where the \u201cconspicuity\u201d of nodules is increased, so that nodules of different sizes and located in parts of the lungs characterized by completely different anatomic noise are more visible. Based on the same assumption the candidates extraction procedure, described in the same chapter, employs a multi-scale method to detect all the nodules of different sizes. Also this step has been compared with two methods ([8] and [1]) described in the literature and tested on the same images. Our implementation of the first one of them ([8]) produced really poor results; the second one obtained a sensitivity ratio (See Appendix C for its definition) equal to 86%. The considerably better performance of our method is proved by the fact that the sensitivity ratio we obtained is much higher (it is equal to 97%) and also the number of False positives detected is much less. The experiments aimed at the classification of the candidates are described in chapter 3; both a rule based technique and 2 learning systems, the Multi Layer Perceptron (MLP) and the Support Vector Machine (SVM), have been investigated. Their input is a set of 16 features. The rule based system obtained the best performance: the cardinality of the set of candidates left is highly reduced without lowering the sensitivity of the system, since no True Positive region is lost. It can be added that this performance is much better than the one of the system used by Ginneken and Schilam in [1], since its sensitivity is lower (equal to 77%) and the number of False Positive left is comparable. The drawback of a rule based system is the need of setting the 9 thresholds used by the rules; since they are experimentally set the system is dependent on the images used to develop it. Therefore it may happen that, on different databases, the performance could not be so good. The result of the MLPs and of the SVMs are described in detail and the ROC analysis is also reported, regarding the experiments performed with the SVMs. Furthermore, the attempt to improve the performance of the classification leaded to other experiments employing SVMs trained with more complicate feature sets. The results obtained, since not better than the previous, showed the need of a proper selection of the features. Future works will then be focused at testing other sets of features, and their combination obtained by means of proper feature selection techniques