Lymph node staging in bladder cancer: role of diffusion weighted magnetic resonant imaging compared to histopathology

Background : lymph node metastasis is important prognostic factor in bladder cancer paients.It also helps in treatment planning.Diffusion weighted magnetic resonance imaging is a new technique for lymph node evaluation depending on tissue cellurality rather than size of lymph nodes. Purpose : The aim of this work is to study the role of DW_MRI in detecting LNs metastasis and staging in bladder cancer. Methods: The study has been carried out at the department of Urology, Zagazig University Hospitals from July 2016 till December 2018. Results: 33 patients with radical cystectomy and lymphadenectomy whom were evaluated by DW-MRI preoperatively. The overall senstivity of DW-MRI was 85.7% and overall specificty was 94.7%. Conclusion: DW-MRI is a safe non invasive technique in lymph node staging in bladder cancer patients with high senstivity and specificty

Computer-aided detection of simultaneous abdominal organ from CT images based on iterative watershed transform

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of the liver, spleen, and kidneys is regarded as a major primary step in computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for abdominal organ segmentation data using mathematical morphology. Our proposed method is based on a hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on a mosaic image and on the computation of the watershed transform. Our algorithm is currently in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen, and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work