Computer Methods For Pulmonary Nodule Characterization From Ct Images

Computed tomography (CT) scans provide radiologists a non-invasive method of imaging internal structures of the body. Although CT scans have enabled the earlier detection of suspicious nodules, these nodules are often small and difficult to accurately classify for radiologists. An automated system was developed to classify a pulmonary nodule based on image features extracted from a single CT scan. Several critical issues related to performance evaluation of such systems were also examined. The image features considered in the system were: statistics from the density distribution, shape, curvature, and boundary features. The shape and density features were computed through moment analysis of the segmented nodule. Local curvature was computed from a triangle-tessellated surface of the nodule; the statistics of the distribution of curvatures were used as features in the system. Finally, the boundary of the nodule was examined to quantify the transition region between the nodule and lung parenchyma. This was accomplished by combining the grayscale information and 3D model to measure the gradient on the surface of the nodule. These methods resulted in a total of 43 features. For compari- son, 2D features were computed for the density and shape features, resulting in 26 features. Four feature classification schemes were evaluated: logistic regression, k-nearest-neighbors, distance-weighted nearest-neighbors, and support vector machines (SVM). These features and classifiers were validated on a large dataset of 259 nodules. The best performance, an area under the ROC curve (AUC) of 0.702, was achieved using 3D features and the logistic regression classifier. A major consideration when evaluating a nodule classification system is whether the system presents an improvement over a baseline performance. Since the majority of large nodules in many datasets are malignant, the impact of nodule size on the performance of the classification system was examined. This was accomplished by comparing the performance of the system with feature sets that included sizedependent features to feature sets that excluded those features.The performance of size alone, estimated using a size-threshold classifier, was an AUC of 0.653. For the SVM classifier, removing size-dependent features reduced the performance from an AUC of 0.69 to 0.61. To approximate the performance that might be obtained on a dataset without a size bias, a subset of cases was selected where the benign and malignant nodules were of similar sizes. On this subset, size was not a very powerful feature with an AUC of 0.507, and features that were not dependent on size performed better than size-dependent features for SVM, with an AUC of 0.63 compared to 0.52. While other methods have been proposed for performing nodule classification, this is the first study to comprehensively look at the performance impact from datasets with nodules that exhibit a bias in size

Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods

The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of CT lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open source, cloud-based, globally distributed, screening CT imaging dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be utilized for clinically relevant AI research.MethodsIn this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.ResultsA total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets

Growth Pattern Analysis of Murine Lung Neoplasms by Advanced Semi-Automated Quantification of Micro-CT Images

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses

Automated Methods For Pulmonary Nodule Growth Rate Measurement: Early Computer-Aided Diagnosis Of Lung Cancer From Computed Tomography Images

Pulmonary nodules are visible as dense, opaque areas in the lung on computed tomography (CT) images and may be early indications of lung cancer. Pulmonary nodule growth rate is highly correlated with malignancy and therefore its evaluation is useful in clinical decision making. Automated methods have been developed for nodule growth rate measurements, but these methods exhibit large measurement error; reducing this error will enable radiologists to make better decisions regarding follow up and treatment, in turn improving patient outcomes. Four major aspects of pulmonary nodule measurement are addressed in this thesis. A formal procedure for the comparative evaluation of different computer algorithms for pulmonary nodule change measurement has been developed that involves a standardized set of 50 CT image pairs and an analysis method. This procedure for the first time addresses the need to be able to quantitatively compare the performance of different methods. A study has been conducted in which developers of 18 computer methods participated and the results form a baseline with which to compare current and future algorithms. Two different computer algorithm approaches were developed to reduce the uncertainty in growth rate measurements. The first approach, moment-based compensation (ZCOMP) was performed on segmented nodule images to address additional observed increased error in the z-direction compared to the xyplane. By applying ZCOMP, volumetric measurement variability was reduced from a 95% limits of agreement of (-24.0%, 18.2%) to (-12.4%, 12.7%) on zerochange nodules imaged on thin-slice scans of the same resolution. The second approach was developed to address difficult-to-segment nodules with complex shapes and attachments. Instead of explicitly segmenting the nodule from the lung parenchyma, the growth index from density method (GID ) uses the density change in a region of interest as a surrogate growth measure. The GID method had much lower variation, (-11.0%, 12.3%) compared to a volumetric segmentation method, (-25.2%, 18.6%). Finally, an automated method was developed for measuring murine pulmonary nodule growth from micro-CT scans, adapting work from methods developed for human patients. This provides improved accuracy for lesion growth measurements used in small animal pre-clinical studies. The method addresses the additional noise, lack of contrast, and poor calibration of micro-CT scans. The measured growth rate was compared to the exponential growth model, and on a dataset of six nodules with repeat scans, the method measured growth that was consistent with the model

Comparison of soft tissue and lung parenchyma densities in a micro-CT scan and a human whole lung CT scan.

<p>Distribution of densities in the lung parenchyma (white) and soft tissue (gray) in (A) a mouse micro-CT scan with adaptive threshold of −190 HU and (B) a human whole lung CT scan with no need for adaptive threshold. The mouse micro-CT scan was obtained at 50 µm with 720 projections. The human whole lung CT scan was from the Weill Cornell Medical College Lung CT database. It was obtained using a GE LightSpeed Ultra scanner at 120 kVp and 80 mA, with 0.7×0.7×1.25 mm³ resolution. The peaks in (A) were not as sharp as those in (B), indicating that the mouse micro-CT scans were noisier than human CT scans. Magnified regions of the lung from (C) a micro-CT scan (yellow circle indicates tumor) and (D) a whole-lung CT scan (red arrow points to tumor) are shown to visualize the difference in scan quality. No scaling was done to the images and each image was windowed for viewing. The scale bars represent 5.0 mm (mouse micro-CT image) or 70.3 mm (human CT image). The color bar range is −750 to 849 HU (mouse micro-CT image) or −1400 to 100 HU (human CT image).</p

Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning

Abstract In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained in cancer screening programs, and thus, opportunistic evaluation of these diseases may be highly valuable. However, manual assessment for each scan is tedious and often subjective, thus we have developed an automatic, rapid computer-aided diagnosis system for emphysema using attention-based multiple instance deep learning and 865 LDCTs. In the task of determining if a CT scan presented with emphysema or not, our novel Transfer AMIL approach yielded an area under the ROC curve of 0.94 ± 0.04, which was a statistically significant improvement compared to other methods evaluated in our study following the Delong Test with correction for multiple comparisons. Further, from our novel attention weight curves, we found that the upper lung demonstrated a stronger influence in all scan classes, indicating that the model prioritized upper lobe information. Overall, our novel Transfer AMIL method yielded high performance and provided interpretable information by identifying slices that were most influential to the classification decision, thus demonstrating strong potential for clinical implementation

Comparison of lung tumor growth measured manually by an observer and by the semi-automated algorithm.

<p>Best linear fit growth curves were plotted for tumors from mouse 2 (left) and mouse 4 tumor A (right) based on measurements by a manual approximation method and by the semi-automated algorithm. The slopes of the best-fit lines for the manual and semi-automated measurements were compared by Student's t-test, and no significant differences were observed between the two slopes (P = 0.62 for mouse 2 and P = 0.57 for mouse 4 tumor A).</p