Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images

Anush Agarwal; Gaikar Rohini; Schieda Nicola; Elfaal Mohamed WalaaEldin; Ukwatta Eranga

doi:10.1117/1.JMI.10.2.024501

20 March 2023 Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images

Anush Agarwal, Gaikar Rohini, Schieda Nicola, Elfaal Mohamed WalaaEldin, Ukwatta Eranga

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 2, 024501 (March 2023). https://doi.org/10.1117/1.JMI.10.2.024501

Abstract

Purpose

Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI).

Approach

In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation.

Results

The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively.

Conclusions

We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.

Citation Download Citation

Anush Agarwal, Gaikar Rohini, Schieda Nicola, Elfaal Mohamed WalaaEldin, and Ukwatta Eranga "Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images," Journal of Medical Imaging 10(2), 024501 (20 March 2023). https://doi.org/10.1117/1.JMI.10.2.024501

Received: 18 May 2022; Accepted: 22 February 2023; Published: 20 March 2023

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