Junhyeok Lee

Junhyeok Lee, Han Jang, Kyu Sung Choi

Precise delineation of meningiomas is crucial for effective radiotherapy (RT) planning, directly influencing treatment efficacy and preservation of adjacent healthy tissues. While automated deep learning approaches have demonstrated considerable potential, achieving consistently accurate clinical segmentation remains challenging due to tumor heterogeneity. Interactive Medical Image Segmentation (IMIS) addresses this challenge by integrating advanced AI techniques with clinical input. However, generic segmentation tools, despite widespread applicability, often lack the specificity required for clinically critical and disease-specific tasks like meningioma RT planning. To overcome these limitations, we introduce Interactive-MEN-RT, a dedicated IMIS tool specifically developed for clinician-assisted 3D meningioma segmentation in RT workflows. The system incorporates multiple clinically relevant interaction methods, including point annotations, bounding boxes, lasso tools, and scribbles, enhancing usability and clinical precision. In our evaluation involving 500 contrast-enhanced T1-weighted MRI scans from the BraTS 2025 Meningioma RT Segmentation Challenge, Interactive-MEN-RT demonstrated substantial improvement compared to other segmentation methods, achieving Dice similarity coefficients of up to 77.6\% and Intersection over Union scores of 64.8\%. These results emphasize the need for clinically tailored segmentation solutions in critical applications such as meningioma RT planning. The code is publicly available at: https://github.com/snuh-rad-aicon/Interactive-MEN-RT

Junhyeok Lee, Yujin Oh, Dahyoun Lee et al.

Acute ischemic stroke (AIS) requires time-critical management, with hours of delayed intervention leading to an irreversible disability of the patient. Since diffusion weighted imaging (DWI) using the magnetic resonance image (MRI) plays a crucial role in the detection of AIS, automated prediction of AIS from DWI has been a research topic of clinical importance. While text radiology reports contain the most relevant clinical information from the image findings, the difficulty of mapping across different modalities has limited the factuality of conventional direct DWI-to-report generation methods. Here, we propose paired image-domain retrieval and text-domain augmentation (PIRTA), a cross-modal retrieval-augmented generation (RAG) framework for providing clinician-interpretative AIS radiology reports with improved factuality. PIRTA mitigates the need for learning cross-modal mapping, which poses difficulty in image-to-text generation, by casting the cross-modal mapping problem as an in-domain retrieval of similar DWI images that have paired ground-truth text radiology reports. By exploiting the retrieved radiology reports to augment the report generation process of the query image, we show by experiments with extensive in-house and public datasets that PIRTA can accurately retrieve relevant reports from 3D DWI images. This approach enables the generation of radiology reports with significantly higher accuracy compared to direct image-to-text generation using state-of-the-art multimodal language models.