Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.
Mingchen GaoUlas BagciLe LuAaron WuMario ButyHoo-Chang ShinHolger RothGeorgios Z PapadakisAdrien DepeursingeRonald M SummersZiyue XuDaniel J MolluraPublished in: Computer methods in biomechanics and biomedical engineering. Imaging & visualization (2016)
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.
Keyphrases
- deep learning
- convolutional neural network
- computed tomography
- interstitial lung disease
- dual energy
- high resolution
- image quality
- machine learning
- contrast enhanced
- positron emission tomography
- systemic sclerosis
- decision making
- magnetic resonance imaging
- randomized controlled trial
- rheumatoid arthritis
- emergency department
- magnetic resonance
- mass spectrometry
- human health
- fluorescence imaging
- neural network