(주) 뷰노

(주) 뷰노

문의사항 남기기
KR EN
VUNO MED INC.
Publications
BACK TO LIST
JRC 2025

Fully Automated Segment-Anything-Model for Robust Lobe Segmentation in Patients with Interstitial Lung Disease

  • Apr. 2025

Abstract

Assessment of lung involvement in computed tomography (CT) images of patients with interstitial lung disease (ILD) is crucial for clinical management. Since the significance of involvement varies across lobes, segmentation of the lobes (left upper, left lower, right upper, right middle, and right lower) is essential. However, deep learning-based lobe segmentation models primarily rely on fissures as key features, which are often not well visualized in CT scans of patients with ILD. Consequently, lobe segmentation models trained on normal lung data often perform poorly on ILD patient data. Moreover, annotating ILD patient data for developing lobe segmentation models is both time-consuming and costly. To address this, we propose a fully supervised approach utilizing the Segment Anything Model (SAM), which has the drawback of requiring manual seed points for target objects. In this study, these seed points are automatically generated based on the output of a lobe segmentation model trained on a normal dataset, using an eroded-contour-based point sampling method. These seed points are then used as inputs for SAM. Additionally, we propose a method to aggregate the output of SAM with single-class segmentation of each lobe for improved robustness. As a result, in three cases with ILD, the dice coefficients of the proposed method showed improvements compared to the original model trained on normal data, from 0.788 to 0.896, 0.786 to 0.869, and 0.795 to 0.845, respectively. Although further validation with larger datasets is needed, this study demonstrates the feasibility of using a fully automated SAM as a pilot study.

 

Link

-

Author

Sungjoo Lim, Doohyun Park, Jonghun Jeong, Changhyun Park, Kyung Min Moon

Tag in

#medical_image

#VUNO Med®-LungCT AI™

이전글
Deep Learning-based Lesion Segmentation for Patients with Usual Interstitial Pneumonia in High-Resolution CT: A pilot study 2025. 05. 02
다음글
Deep Learning Based Computer Aided Diagnosis System for Usual Interstitial Pneumonia in Chest HRCT 2025. 05. 02
List

이 웹사이트는 원활한 서비스 제공을 위하여 당사 개인정보 처리방침에 명시된 쿠키를 사용합니다.

만일 당사의 쿠키 사용 정책에 대해 더 알고 싶으신 경우에는 개인정보 처리방침 을 참고하여 주시기 바랍니다.