Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrate state-of-the-art classification accuracy under the patch-based classification and shows the potential of predicting the ILD type using holistic image.
Abstract. Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves expert qualitative ratings on several criteria describing a nodule's appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper, we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics, and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset, achieving high accuracy. By providing lung nodule characterization, this method can provide a robust alternative reference opinion for lung cancer diagnosis.
We present a new image quality assessment method for determining whether
reducing radiation dose impairs the image quality of computed tomography (CT) in
qualitative and quantitative clinical analyses tasks. In this Institutional
Review Board-exempt study, we conducted a review of 50 patients (male, 22;
female, 28) who underwent reduced-dose CT scanning on the first follow-up after
standard-dose multiphase CT scanning. Scans were for surveillance of von
Hippel-Lindau disease (N = 26) and renal cell carcinoma (N =
10). We investigated density, morphometric, and structural differences between
scans both at tissue (fat, bone) and organ levels (liver, heart, spleen, lung).
To quantify structural variations caused by image quality differences, we
propose using the following metrics: dice similarity coefficient, structural
similarity index, Hausdorff distance, gradient magnitude similarity deviation,
and weighted spectral distance. Pearson correlation coefficient and Welch
2-sample t test were used for quantitative comparisons of organ
morphometry and to compare density distribution of tissue, respectively. For
qualitative evaluation, 2-sided Kendall Tau test was used to assess agreement
among readers. Both qualitative and quantitative evaluations were designed to
examine significance of image differences for clinical tasks. Qualitative
judgment served as an overall assessment, whereas detailed quantifications on
structural consistency, intensity homogeneity, and texture similarity revealed
more accurate and global difference estimations. Qualitative and quantitative
results indicated no significant image quality degradation. Our study concludes
that low(er)-dose CT scans can be routinely used because of no significant loss
in quantitative image information compared with standard-dose CT scans.
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