轻量级模型实现3DCT肿瘤体积分割，提升治疗评估精度。
Lite ENSAM: a lightweight cancer segmentation model for 3D Computed Tomography
- 基于RECIST标注数据，优化轻量化网络结构进行体积分割
- 在测试集上达60.7%的Dice系数和63.6%的表面相似度
- 推理速度快、内存占用低，适合临床实时应用
准确测量肿瘤大小是评估癌症治疗反应的关键。目前最广泛采用的标准是实体瘤疗效评价标准（RECIST）v1.1，依赖单平面最长直径测量。然而，体积测量能更可靠地反映治疗效果，其临床应用受限于手动体积标注的高劳动成本。本文提出Lite ENSAM，一种针对3D CT扫描中基于RECIST标注数据的轻量化肿瘤体积分割模型。该模型提交至MICCAI FLARE 2025任务1：跨癌种CT图像分割，子任务2，在隐藏测试集上获得60.7%的Dice相似系数（DSC）和63.6%的归一化表面Dice（NSD），在公开验证集上平均总内存使用量为50.6 GB，平均推理时间为14.4秒（运行于CPU）。
原文摘要 · Abstract (English)
Accurate tumor size measurement is a cornerstone of evaluating cancer treatment response. The most widely adopted standard for this purpose is the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1, which relies on measuring the longest tumor diameter in a single plane. However, volumetric measurements have been shown to provide a more reliable assessment of treatment effect. Their clinical adoption has been limited, though, due to the labor-intensive nature of manual volumetric annotation. In this paper, we present Lite ENSAM, a lightweight adaptation of the ENSAM architecture designed for efficient volumetric tumor segmentation from CT scans annotated with RECIST annotations. Lite ENSAM was submitted to the MICCAI FLARE 2025 Task 1: Pan-cancer Segmentation in CT Scans, Subtask 2, where it achieved a Dice Similarity Coefficient (DSC) of 60.7% and a Normalized Surface Dice (NSD) of 63.6% on the hidden test set, and an average total RAM time of 50.6 GBs and an average inference time of 14.4 s on CPU on the public validation dataset.
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