Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

The classification of benign versus malignant lung nodules using chest CT plays a pivotal role in the early detection of lung cancer and this early detection has the best chance of cure. Although deep learning is now the most successful solution for image classification problems, it requires a myriad number of training data, which are not usually readily available for most routine medical imaging applications. In this paper, we propose the transferable multi-model ensemble (TMME) algorithm to separate malignant from benign lung nodules using limited chest CT data. This algorithm transfers the image representation abilities of three ResNet-50 models, which were pre-trained on the ImageNet database, to characterize the overall appearance, heterogeneity of voxel values and heterogeneity of shape of lung nodules, respectively, and jointly utilizes them to classify lung nodules with an adaptive weighting scheme learned during the error back propagation. Experimental results on the benchmark LIDC-IDRI dataset show that our proposed TMME algorithm achieves a lung nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches

Concepts of medical imaging in the work up and follow-up of cancer : oncological imaging at a glance.

editorial reviewedOncological imaging is a subspecialty of medical imaging and focuses on the workup and the follow-up of cancer. Oncological imaging takes into account all the specificities of cancer diseases, which is a constantly evolving field, especially in the era of precision medicine, and plays a key role in the care of cancer patients. It permits reliable diagnosis and gives precious information concerning disease extension at diagnosis, which is essential for the treatment planning. Oncological imaging allows also followup of patients under treatment, using response evaluation scores. Interventional imaging, which provides minimally invasive procedures, is useful in order to obtain a histological diagnosis, to treat some tumour or to improve quality of life of cancer patients. Finally, numerous perspectives, among them the advent of artificial intelligence (radiomics), will further strengthen the role of oncologic imaging in the near future

Classification of malignant and benign lung nodule and prediction of image label class using multi-deep model

Lung cancer has been listed as one of the world’s leading causes of death. Early diagnosis of lung nodules has great significance for the prevention of lung cancer. Despite major improvements in modern diagnosis and treatment, the five-year survival rate is only 18%. Before diagnosis, the classification of lung nodules is one important step, in particular, because automatic classification may help doctors with a valuable opinion. Although deep learning has shown improvement in the image classifications over traditional approaches, which focus on handcraft features, due to a large number of intra-class variational images and the inter-class similar images due to various imaging modalities, it remains challenging to classify lung nodule. In this paper, a multi-deep model (MD model) is proposed for lung nodule classification as well as to predict the image label class. This model is based on three phases that include multi-scale dilated convolutional blocks (MsDc), dual deep convolutional neural networks (DCNN A/B), and multi-task learning component (MTLc). Initially, the multi-scale features are derived through the MsDc process by using different dilated rates to enlarge the respective area. This technique is applied to a pair of images. Such images are accepted by dual DCNNs, and both models can learn mutually from each other in order to enhance the model accuracy. To further improve the performance of the proposed model, the output from both DCNNs split into two portions. The multi-task learning part is used to evaluate whether the input image pair is in the same group or not and also helps to classify them between benign and malignant. Furthermore, it can provide positive guidance if there is an error. Both the intra-class and inter-class (variation and similarity) of a dataset itself increase the efficiency of single DCNN. The effectiveness of mentioned technique is tested empirically by using the popular Lung Image Consortium Database (LIDC) dataset. The results show that the strategy is highly efficient in the form of sensitivity of 90.67%, specificity 90.80%, and accuracy of 90.73%

Erstellung quantitativer Imaging-Biomarker zur Detektion von fibrosiertem Lungengewebe im HR-CT

Ziel dieser Arbeit war die Erstellung von Imaging-Biomarkern, die robust zwischen fibrosiertem und gesundem Lungengewebe im HR-CT unterscheiden können. Mit Hilfe morphologischer Bildverarbeitungsverfahren sollten die Charakteristika von fibrosiertem Lungengewebe verstärkt und numerisch ausgewertet werden. Hierfür wurden HR-CT-Bilddaten von 78 Patienten mit fibrosiertem Lungengewebe und 23 Patienten mit unauffälligem Lungenparenchym untersucht. Zu Beginn erfolgte eine semiautomatische Segmentierung der Schnittbilder, um anschließend die für fibrosiertes Lungengewebe typischen morphologischen Charakteristika hervorzuheben. Da diese Merkmale bei Lungengesungen nicht vorliegen, führt diese Bearbeitung zu einer Verstärkung der Unterschiede zwischen Patienten mit Lungenfibrose und Lungengesunden. Dazu erfolgte nach der Segmentierung im zweiten Schritt eine Binarisierung durch drei festgelegte Schwellenwertbereiche, um morphologische Charakteristika des Lungengewebes hervorzuheben. Im dritten Schritt wurden diese Merkmale durch vier verschiedene Kombinationen aus Closing- und Opening-Strukturelementen verstärkt. Die Ergebnisse zeigen für alle vier erstellten Parameter statistisch hoch signifikante Gruppenunterschiede. Zur Klassifizierung der insgesamt 39 Datenreihen wurden, auf einer logistischen Regressionsanalyse basierend, die AUC-Werte der ROC-Kurven bestimmt. Eine erste Auswertung erfolgte nach der Binarisierung und eine zweite Auswertung nach der Verstärkung der morphologischen Charakteristika durch Closing- und Opening-Operationen. Schon nach der Binarisierung zeigten sich AUC-Werte bis zu 0,976. Der analysierte Schwellenwertbereich S3-500; -200 HE brachte für die meisten Parameter die prädiktivsten Werte hervor. Der Parameter Cluster-Pixel/CT-Seg-Pixel im Schwellenwertbereich S3 mit einem 5×5 Pixel großem Closing- und einem 3×3 Pixel großem Opening-Strukturelement erreichte einen maximalen AUC-Wert von 0,989. Durch den ersten Arbeitsschritt der Binarisierung konnte im Schwellenwertbereich S3 robust zwischen den Gruppen mit fibrosiertem und gesundem Lungengewebe unterschieden werden. Die Verstärkung der morphologischen Charakteristika konnte eine zusätzliche Verbesserung der AUC-Werte erzielen. Eingebettet in ein Konzept, wie z. B. von Radiomics, stellt dieses Modell einen funktionierenden und robusten Imaging-Biomarker dar.The purpose of this study was the creation of imaging biomarkers to distinguish patients with pulmonary fibrosis and healthy subjects in high-resolution CTs. Intensification of characteristics of pulmonary fibrosis, based on morphological image processing, was performed and evaluated numerically. Datasets of 78 patients with the diagnosis of pulmonary fibrosis and 23 healthy subjects were studied and compared. At first semiautomatic lung parenchyma segmentation was performed. After that, the segmented images were used to isolate and to enhance specif- ic morphological fibrosis characteristics. Next, binary images were created for each of three different Hounsfield unit (HU) threshold ranges. To intensify the morphological fibrosis characteristics, basic image processing methods like morphological opening and closing were applied, leading to different, parameterized image variations of the normal and the fibrotic group. Statistical analysis shows significant differences, between both patient groups, for all parameter settings. In order to find the best parameter settings, a logistic regression analysis was implemented and the values of the area under the receiver operator charac- teristic (ROC) curve (AUC) were computed. A first group comparison was performed after the binarization of the images and a second comparison after the enhancement of the specific fibrosis characteristics by the closing and opening operations. Based on the analysis of binary image, AUC values of up to 0,976 could be found. The threshold range of -500 to -200 HU lead to the highest AUC values. Application of the morpho- logical image processing operations lead to a slight increase up to AUC values being 0,989. These findings suggest that the evaluated image processing operations can serve as a valuable numerical image marker for a reproducible, and observer independent differen- tiation between normal and fibrotic lung parenchyma. The concept fits well into the current radiomics philosophy

Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging