bibtext: @InProceedings{ Kho_Unified_MICCAI2024,
                   author = { Khor, Hee Guan and Yang, Xin and Sun, Yihua and Wang, Jie and Huang, Sijuan and Wang, Shaobin and Lu, Bai and Ma, Longfei and Liao, Hongen },
                   title = { { Unified Prompt-Visual Interactive Segmentation of Clinical Target Volume in CT for Nasopharyngeal Carcinoma with Prior Anatomical Information } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: The delineation of the Clinical Target Volume (CTV) is a crucial step in the radiotherapy (RT) planning process for patients with nasopharyngeal carcinoma (NPC). However, manual delineation is labor-intensive, and automatic CTV contouring for NPC is difficult due to the nasopharyngeal complexity, tumor variability, and judgement-based criteria. To address the above-mentioned problems, we introduce SAM-RT, the first large vision model (LVM) designed for CTV contouring in NPC. Given the anatomical dependency required for CTV contouring—which encapsulates the Gross Tumor Volume (GTV) while minimizing exposure to Organs-at-Risk (OAR)—our approach begins with the fine-tuning of the Segment Anything Model (SAM), using a Low-Rank Adaptation (LoRA) strategy for segmenting GTV and OAR across multi-center and multi-modality datasets. This step ensures SAM-RT initially integrates with anatomical prior knowledge for CTV contouring. To optimize the use of previously acquired knowledge, we introduce Sequential LoRA (SeqLoRA) to improve knowledge retention in SAM-RT during the fine-tuning for CTV contouring. We further introduce the Prompt-Visual Cross Merging Attention (ProViCMA) for enhanced image and prompt interaction, and the Gate-Regulated Prompt Adjustment (GaRPA) strategy, utilizing learnable gates to direct prompts for effective CTV task adaptation. Efficient utilization of knowledge across relevant datasets is essential due to sparse labeling of medical images for specific tasks. To achieve this, SAM-RT is trained using an information-querying approach. SAM-RT incorporates various prior knowledge: 1) Reliance of CTV on GTV and OAR, and 2) Eliciting expert knowledge in CTV contouring. Extensive quantitative and qualitative experiments validate our designs.
title: Unified Prompt-Visual Interactive Segmentation of Clinical Target Volume in CT for Nasopharyngeal Carcinoma with Prior Anatomical Information
authors: Khor, Hee Guan Yang, Xin Sun, Yihua Wang, Jie Huang, Sijuan Wang, Shaobin Lu, Bai Ma, Longfei Liao, Hongen