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The high volume and quality of multi-parameter magnetic resonance imaging (mp-MRI) data containing clinically significant (CS) prostate cancer (PCa) are critical for automated PCa detection with a high accuracy. However, mp-MRI data of CS PCa is scarce and costly to obtain in practice. This projects focuses on designing novel Generative Adversarial Networks (GANs) for synthesizing high-quality mp-MRI images of CS PCa.
Code is available at [Github]
Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection and diagnosis of PCa in mp-MRI images are highly desirable. In this work we introduce a series of our recent works on utilizing deep convolutional neural networks (CNN) for automated PCa detection and diagnosis.
Retinal fundus images provide rich information about pathological changes, which can be used for diagnosis of eye-related diseases, su
ch as macular degeneration, diabetic retinopathy and glaucoma. Among various features in fundus images, retinal vessel features play a crucial role in diagnosis. Taking diabetic retinopathy as an example, microaneurysm, one fundamental symptom, generally exists along retinal vessels. For the extraction of retinal vessel features, generating accurate segmentation of retinal blood vessels is essential. However, manual annotation by a human observer is time consuming. Automated retinal vessel segmentation has been widely studied over decades; however it remains a challenging task especially for thin vessels. In addition, due to the inter-observer problem, a better evaluation metric is highly demanding.
We propose a method for automated renal segmentation which consists of three main steps. First, the whole kidney is segmented based on the concept of Maximally Stable Temporal Volume (MSTV). The proposed MSTV detects an-atomical structures that are stable in both spatial domain and temporal dynam-ics. MSTV-based kidney segmentation is robust to noises and does not require a training phase. It can well adapt to kidney shape variations caused by renal dys-function. Second, voxels in the segmented kidney are described by principal components (PCs) to remove temporal redundancy and noises. And then k-means clustering of PCs is applied to separate voxels into cortex, medulla and pelvis. Third, a refinement method is introduced to further remove noises in each segmented compartment.
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