Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

• URL: http://arxiv.org/abs/2606.04365v1
• Date: Wed, 03 Jun 2026 02:28:57 GMT
• Title: Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
• Abstract summary: We reformulate kidney CT characterization as a per-lesion set-prediction task.<n>We curated 2,619 CT volumes from 788 patients at one academic medical center.<n>We propose textbfLesionDETR, a DETR-style architecture with size-distance Hungarian matching.
• Score: 11.709061205652871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \textbf{LesionDETR}, a DETR-style architecture with size-distance Hungarian matching and a hierarchical loss that aggregates per-slot outputs to side-level objectives. Across four input representations and six encoder initializations, two design choices dominate: a segmentation mask as an input channel, and same-domain abdominal pretraining (SuPreM); generic large-corpus pretraining is no better than random initialization. LesionDETR reaches bilateral side-level abnormality AUC $0.799 \pm 0.009$ on UF-Health and $0.817 \pm 0.072$ on KiTS23. A count-conditioned variant reaches per-lesion mAP $0.190 \pm 0.083$ on cystic lesions; rare solid-lesion AP stays at the noise floor, pointing to targeted data collection, not architecture, as the next bottleneck. The framework yields verified per-lesion predictions for downstream structured report generation.

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