Universal lymph node detection in T2 MRI using neural networks

Abstract

Purpose: Identification of lymph nodes (LNs) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) is critical for assessment of lymphadenopathy. Prior work on LN detection has been limited to specific anatomical regions of the body (pelvis, rectum). Therefore, an approach to universally detect both benign and metastatic nodes in T2 MRI studies of the body is highly desirable.

Methods: We developed a Computer Aided Detection (CAD) pipeline to universally identify LN in T2 MRI. First, we trained various neural networks for detecting LN: Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS, FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that VFNet with Adaptive Training Sample Selection (ATSS) outperformed Faster RCNN with HNEM. Finally, we ensembled models that surpassed a 45% mAP threshold.

Results: Experiments on 122 test studies revealed that VFNet achieved a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at 4FP. We found that VFNet and the one-stage model ensemble can be interchangeably used in the CAD pipeline.

Conclusion: Our CAD pipeline universally detected both benign and metastatic nodes in T2 MRI studies, resulting in a sensitivity improvement of [Formula: see text]14% over the current LN detection approaches (sensitivity of 78.7% at 4 FP vs. 64.6% at 5 FP per volume).

Keywords: Deep learning; Detection; Lymph node; MRI; T2.