Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
This work addresses lung cancer diagnosis by providing a robust alternative reference opinion for clinicians, though it is incremental as it builds on existing feature extraction and classification methods.
The paper tackled the problem of evaluating lung nodule malignancy from CT scans by developing a computerized approach that combines shape modeling with spherical harmonics and appearance features from deep CNNs, achieving high accuracy on the Lung Image Database Consortium dataset.
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.