Implementation of Computer-Aided Diagnosis for Liver Nodular Lesions in Gd-EOB-DTPA Enhanced MRI

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K20215
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 90130:Medical systems-related
• Research Institution: The University of Tokyo
• Principal Investigator: Takenaga Tomomi 東京大学, 医学部附属病院, 特任助教 (80779786)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: radiology report / computer-aided diagnosis / segmentation / classification / detection / Gd-EOB-DTPA enhanced MRI / liver nodular lesions / liver segments

Outline of Final Research Achievements

This study aims to develop and implement an automated system for the detection, classification, and report generation of liver nodular lesions in MR images enhanced with Gd-EOB-DTPA (EOB-MR images). During the research period, the following objectives were accomplished:
(1) Development of a computer-aided diagnosis system that simultaneously detects and classifies liver nodular lesions in EOB-MR images. (2) Improvement of detection and classification performance through data enrichment. (3) Automatic extraction of liver segments using deep learning, based on rule-based methods for liver segment extraction. (4) Automated generation of reading reports.

Free Research Field

医用画像処理

Academic Significance and Societal Importance of the Research Achievements

最終的に達成した読影レポートの自動生成は、単純なルールベースな手法で構造化レポートを自動生成したものではあるが、他の手法による構造化レポート自動生成の評価や正解としての利用が可能であるため、重要な結果であると考える。自動生成を試みた読影レポートは実際に読影医の作業を取って代われるほどのものではないが、この試みにより、診断を行う際の負担軽減や診断精度の向上が図られることが期待される。