AIMC Topic: Urinary Bladder

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Robot-assisted laparoscopic augmentation ileocystoplasty and excision of an intraperitoneal mass: a case report.

The Journal of international medical research
OBJECTIVE: This case is reported to introduce an advanced surgical technique and share our experience with surgeons.

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Medical image analysis
Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intr...

Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data.

PloS one
We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especial...

U-Net based deep learning bladder segmentation in CT urography.

Medical physics
OBJECTIVES: To develop a U-Net-based deep learning approach (U-DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer-assisted bladder cancer detection and treatment response assessment pipeline.

Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.

Medical physics
PURPOSE: We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urography (CTU).

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned proble...