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Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis in computer-aided diagnosis systems. In recent years, segmentation methods based on fully convolutional networks (FCNs) have achieved great success in general images. FCN performance is primarily due to it leveraging large labeled datasets to hierarchically learn the features that correspond to the shallow appearance as well as the deep semantics of the images. However, such dependence on large dataset does not translate well into medical images where there is a scarcity of annotated medical training data, and FCN results in coarse ROI detections and poor boundary definitions. To overcome this limitation, medical image-specific FCN methods have been introduced with post-processing techniques to refine the segmentation results; however, the performance of these methods is reliant on the appropriate tuning of a large number of parameters and dependence on data-specific post-processing techniques. In this study, we leverage the state-of-the-art image feature learning method of generative adversarial network (GAN) for its inherent ability to produce consistent and realistic images features by using deep neural networks and adversarial learning concept. We improve upon GAN such that ROI features can be learned at different levels of complexities (simple and complex), in a controlled manner, via our proposed dual-path adversarial learning (DAL). The outputs from our DAL are then augmented to the learned ROI features into the existing FCN training data, which increases the overall feature diversity. We conducted experiments on three public datasets with a variety of visual characteristics. Our results demonstrate that our DAL can improve FCN-based segmentation methods and outperform or be competitive in performances to the state-of-the-art methods without using medical image-specific optimizations. [ABSTRACT FROM AUTHOR]

The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]