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The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

No abstract

Renal lymphangiectasia is a rare benign condition of the kidney without specific clinical presentations. Classic imaging findings are described in literature. Here, we present a case of renal lymphangiectasia with history of bilateral flank pain and abnormal renal function tests. The radiological appearance on ultrasound (US) and computed tomography (CT) showed features of bilateral renal lymphangiectasia but the patient refused invasive procedure for aspiration of the cysts. So, follow-up of the patient was done by magnetic resonance imaging (MRI). Imaging findings of our case on US, CT, and MRI are discussed along with details of the additional finding of dilated retroperitoneal lymphatic channels, cisterna chyli, as well as the thoracic duct.