2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
This work addresses a critical need in medical imaging for cancer diagnosis by providing an efficient detection method, though it is incremental as it builds on existing 2D detection and aggregation techniques.
The paper tackles automated lymph node detection in CT scans by decomposing the 3D problem into 2D detection subtasks using HOG features and linear classifiers, achieving sensitivities of 78.0% at 6 FP/vol. for mediastinal and 73.1% at 6 FP/vol. for abdominal datasets.
Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.