Journal of medical imaging (Bellingham, Wash.) · 2026
Deep learning-based mediastinal lymph node assessment on PET/CT images without pixel-level annotations.
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Engelson Sofija, Elser Yannic, Sieren Malte Maria, Ehrhardt Jan, Andresen Julia, Schierholz Stefanie, Keck Tobias, Drömann Daniel, Barkhausen Jörg, Handels Heinz
Аннотация
PURPOSE: N -staging, a critical component in cancer diagnostics, quantifies metastatic involvement of lymph nodes and plays an important role in guiding treatment decisions. Manual assessment of lymph nodes on PET/CT scans is time-consuming due to minimal contrast to surrounding tissue and strong heterogeneity of the lymph node's morphology. To streamline the N -staging process, we propose a deep learning-based algorithm that localizes lymph node stations through atlas-to-patient registration, classifies mediastinal lymph node stations as malignant or benign, and subsequently performs automated N -staging. Notably, our model is trained without any pixel-level annotations, i.e., using image-level classification labels only.
APPROACH: To address the challenge of training without annotations at the pixel level, we use prior knowledge of the lymph node station locations through atlas-to-patient registration and deduce pseudo-labels for lymph node station groups from the N -stage to enable weakly supervised network training.
RESULTS: The proposed algorithm achieves an accuracy of 0.88 ± 0.02 , a sensitivity of 0.72 ± 0.08 , and a specificity of 0.90 ± 0.03 for lymph node station classification, which is significantly better than the performance of the standard threshold-based approach used for lymph node assessment in radiological images and an algorithm for PET lesion segmentation that was trained with segmentation masks. For automatic N -staging, the accuracy of 0.63 ± 0.04 is on par with an algorithm that was trained with segmentation masks.
CONCLUSIONS: The division of the problem setting into subtasks as well as the integration of prior knowledge enables better or comparable performance of models trained with and without segmentation masks.
APPROACH: To address the challenge of training without annotations at the pixel level, we use prior knowledge of the lymph node station locations through atlas-to-patient registration and deduce pseudo-labels for lymph node station groups from the N -stage to enable weakly supervised network training.
RESULTS: The proposed algorithm achieves an accuracy of 0.88 ± 0.02 , a sensitivity of 0.72 ± 0.08 , and a specificity of 0.90 ± 0.03 for lymph node station classification, which is significantly better than the performance of the standard threshold-based approach used for lymph node assessment in radiological images and an algorithm for PET lesion segmentation that was trained with segmentation masks. For automatic N -staging, the accuracy of 0.63 ± 0.04 is on par with an algorithm that was trained with segmentation masks.
CONCLUSIONS: The division of the problem setting into subtasks as well as the integration of prior knowledge enables better or comparable performance of models trained with and without segmentation masks.