用RECIST标记指导3DCT肿瘤分割，轻量高效且精度高。
LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation
- 以RECIST线段为提示，生成3D病灶区域裁剪输入
- 在公开验证集上达79.4的Dice系数，测试集73.9
- 推理仅需6.9秒，内存占用3.6GB，适合临床部署
RECIST直径测量广泛用于肿瘤反应评估，但仅提供病灶范围的有限2D描述。本文提出轻量级的LETT-NeXt模型，从CT体积和RECIST标记预测3D病灶掩码，用于CVPR 2026基于基础模型的泛癌种CT图像分割竞赛。LETT-NeXt提取以RECIST为中心的区域裁剪，将RECIST线及其端点编码为两个提示通道，并与CT输入拼接。采用紧凑的MedNeXt-v2编码器-解码器进行分割，后续结合提示感知组件选择与自适应AutoZoom推理。在公开验证集上，模型取得79.4±10.1的骰子相似系数（DSC）和72.3±16.2的归一化表面骰子（NSD）；在隐藏测试集上，分别达到73.9和67.3，挑战得分70.6%。在公开验证镜像数据上，单例病例推理耗时6.9±3.0秒，峰值内存占用3.6 GB。代码已开源于github.com/Ahus-AIM/lett-next。
原文摘要 · Abstract (English)
RECIST diameter measurements are widely used for tumor response assessment, but they provide only a limited 2D description of lesion extent. We present LETT-NeXt, a lightweight RECIST-guided model that predicts 3D lesion masks from CT volumes and RECIST markers for the CVPR 2026 Foundation Models for Pan-cancer Segmentation in CT Images competition. LETT-NeXt extracts a RECIST-centered regional crop, encodes the RECIST line and endpoints as two prompt channels, and concatenates them with the CT input. A compact MedNeXt-v2 encoder--decoder predicts the lesion mask, followed by prompt-aware component selection and adaptive AutoZoom inference. On the public validation set, LETT-NeXt achieved a Dice Similarity Coefficient (DSC) of 79.4 $\pm$ 10.1 and a Normalized Surface Dice (NSD) of 72.3 $\pm$ 16.2. On the hidden test set, it achieved a DSC of 73.9 and an NSD of 67.3, corresponding to a challenge score of 70.6\%. On the public validation mirror, LETT-NeXt completed CPU inference in 6.9 $\pm$ 3.0 s per case with a peak memory use of 3.6 GB. Code is available at github.com/Ahus-AIM/lett-next.
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