CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network
نویسندگان
چکیده
منابع مشابه
Improving Fully Convolution Network for Semantic Segmentation
Fully Convolution Networks (FCN) have achieved great success in dense prediction tasks including semantic segmentation. In this paper, we start from discussing FCN by understanding its architecture limitations in building a strong segmentation network. Next, we present our Improved Fully Convolution Network (IFCN). In contrast to FCN, IFCN introduces a context network that progressively expands...
متن کاملNovel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prio...
متن کاملSatellite Imagery Classification Based on Deep Convolution Network
Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at t...
متن کاملA new CT prostate segmentation for CT-based HDR brachytherapy
High-dose-rate (HDR) brachytherapy has become a popular treatment modality for localized prostate cancer. Prostate HDR treatment involves placing 10 to 20 catheters (needles) into the prostate gland, and then delivering radiation dose to the cancerous regions through these catheters. These catheters are often inserted with transrectal ultrasound (TRUS) guidance and the HDR treatment plan is bas...
متن کامل3D Deep Convolution Neural Network Application in Lung Nodule Detection on CT Images
Pulmonary cancer is the leading cause of cancer-related death worldwide, and early stage of pulmonary cancer detection using low-dose computed tomography (CT) could prevent millions of patients being killed every year. However, reading millions of those CT scans is an enormous burden for radiologists. Therefore, an immediate need is to read, detect and evaluation CT scans automatically and fast...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Medical Physics
سال: 2019
ISSN: 0094-2405,2473-4209
DOI: 10.1002/mp.13933