Paper Info
Title
CT Data Curation for Liver Patients - Phase Recognition in Dynamic Contrast-Enhanced CT.
Abstract
As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital picture archiving and communication systems (PACSs). This is the focus of our work, where we present a principled data curation tool to extract multi-phase computed tomography (CT) liver studies and identify each scan’s phase from a real-world and heterogenous hospital PACS dataset. Emulating a typical deployment scenario, we first obtain a set of noisy labels from our institutional partners that are text mined using simple rules from DICOM tags. We train a deep learning system, using a customized and streamlined 3D squeeze and excitation (SE) architecture, to identify non-contrast, arterial, venous, and delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as “contrast”. Extensive experiments on a dataset of 43K scans of 7680 patient imaging studies demonstrate that our 3DSE architecture, armed with our aggregated loss, can achieve a mean F1 of 0.977 and can correctly harvest up to \(92.7\%\) of studies, which significantly outperforms the text-mined and standard-loss approach, and also outperforms other, and more complex, model architectures.
Year
DOI
Venue
2019
10.1007/978-3-030-33391-1_16
DART/MIL3ID@MICCAI
Field
DocType
Citations 
Liver Scan,Cross entropy,Data mining,DICOM,Medical imaging,Computer science,Communications system,Filter (signal processing),Data curation,Artificial intelligence,Deep learning
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Bo Zhou1108.26
Adam P. Harrison210117.06
Jiawen Yao316813.10
Chi-Tung Cheng411.37
jing xiao58042.68
Chien-Hung Liao611.71
Le Lu7129786.78