Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Hyeonsoo Moon; Yuankai Huo; Richard G. Abramson; Richard Alan Peters; Albert Assad; Tamara K. Moyo; Michael R. Savona; Bennett A. Landman

doi:10.1016/j.compbiomed.2019.01.018

JOURNAL ARTICLE

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Hyeonsoo Moon Yuankai Huo Richard G. Abramson Richard Alan Peters Albert Assad Tamara K. Moyo Michael R. Savona Bennett A. Landman

Year: 2019 Journal: Computers in Biology and Medicine Vol: 107 Pages: 109-117 Publisher: Elsevier BV

DOI: 10.1016/j.compbiomed.2019.01.018

Get Full-Text PDF Get Analytical Report

Keywords:

Pipeline (software) Segmentation Computer science Artificial intelligence Preprocessor Deep learning Process (computing) Computer vision Image segmentation Volume (thermodynamics) End-to-end principle

Metrics

Cited By

2.12

FWCI (Field Weighted Citation Impact)

Refs

0.85

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Radiomics and Machine Learning in Medical Imaging

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Autopsy Techniques and Outcomes

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Abdominal Trauma and Injuries

Health Sciences → Medicine → Surgery

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Metrics

Citation History

Topics

Related Documents

End-to-end deep learning pipeline for on-board extraterrestrial rock segmentation

Deep Label Fusion: A 3D End-To-End Hybrid Multi-atlas Segmentation and Deep Learning Pipeline

Automated Classification Using End-to-End Deep Learning

An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Incorporating Deep Learning Model Development With an End-to-End Data Pipeline