Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning

Bousabarah, Khaled, Letzen, Brian, Tefera, Jonathan, Savic, Lynn, Schobert, Isabel, Schlachter, Todd, Staib, Lawrence H., Kocher, Martin, Chapiro, Julius and Lin, MingDe . Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom. Radiol.. NEW YORK: SPRINGER. ISSN 2366-0058

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DOI:10.1007/s00261-020-02604-5

Abstract

Purpose Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically. Methods In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR). Results 73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations. Conclusion Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS.

Item Type:

Journal Article

Creators:

Creators	Email	ORCID	ORCID Put Code
Bousabarah, Khaled	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Letzen, Brian	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Tefera, Jonathan	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Savic, Lynn	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Schobert, Isabel	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Schlachter, Todd	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Staib, Lawrence H.	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Kocher, Martin	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Chapiro, Julius	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Lin, MingDe	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED