Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks
نویسندگان
چکیده
منابع مشابه
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on...
متن کاملAutomatic segmentation of glioma tumors from BraTS 2018 challenge dataset using a 2D U-Net network
Background: Glioma is the most common primary brain tumor, and early detection of tumors is important in the treatment planning for the patient. The precise segmentation of the tumor and intratumoral areas on the MRI by a radiologist is the first step in the diagnosis, which, in addition to the consuming time, can also receive different diagnoses from different physicians. The aim of this study...
متن کاملU-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables prec...
متن کاملAutomatic skin lesion segmentation with fully convolutional-deconvolutional networks
This paper summarizes our method and validation results for the ISBI Challenge 2017 Skin Lesion Analysis Towards Melanoma Detection Part 1: Lesion Segmentation.
متن کاملFully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
We present a method to address the challenging problem of segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localization FCN and a segmentation FCN. More specifically, in the first step we train a regression 3D FCN (we call it “LocalizationNet”) to find the bounding box of ...
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: Energies
سال: 2020
ISSN: 1996-1073
DOI: 10.3390/en13040829