From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans

• URL: http://arxiv.org/abs/2503.07933v2
• Date: Thu, 13 Mar 2025 00:01:12 GMT
• Title: From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans
• Authors: Qinji Yu, Yirui Wang, Ke Yan, Dandan Zheng, Dashan Ai, Dazhou Guo, Zhanghexuan Ji, Yanzhou Su, Yun Bian, Na Shen, Xiaowei Ding, Le Lu, Xianghua Ye, Dakai Jin,
• Abstract summary: LN-Tracker is a novel LN tracking transformer for joint end-to-end detection and 3D instance association.<n>LN-Tracker decouples transformer decoder's query into the track and detection groups, where the track query autoregressively follows previously tracked LN instances.<n>Extensive evaluation on four lymph node datasets shows LN-Tracker's superior performance, with at least 2.7% gain in average sensitivity when compared to other top 3D/2.5D detectors.
• Score: 9.00859316589341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging, treatment planning and beyond. Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate effectiveness of 2.5D approaches (i.e, using 2D network with multi-slice inputs), leveraging pretrained 2D model weights and showing improved accuracy as compared to separate 2D or 3D detectors. However, slice-based 2.5D detectors do not explicitly model inter-slice consistency for LN as a 3D object, requiring heuristic post-merging steps to generate final 3D LN instances, which can involve tuning a set of parameters for each dataset. In this work, we formulate 3D LN detection as a tracking task and propose LN-Tracker, a novel LN tracking transformer, for joint end-to-end detection and 3D instance association. Built upon DETR-based detector, LN-Tracker decouples transformer decoder's query into the track and detection groups, where the track query autoregressively follows previously tracked LN instances along the z-axis of a CT scan. We design a new transformer decoder with masked attention module to align track query's content to the context of current slice, meanwhile preserving detection query's high accuracy in current slice. An inter-slice similarity loss is introduced to encourage cohesive LN association between slices. Extensive evaluation on four lymph node datasets shows LN-Tracker's superior performance, with at least 2.7% gain in average sensitivity when compared to other top 3D/2.5D detectors. Further validation on public lung nodule and prostate tumor detection tasks confirms the generalizability of LN-Tracker as it achieves top performance on both tasks.

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