pith:RQLNAZPL
SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound
SAMe turns one external body image into patient-specific organ locations and 6-DoF probe starting positions for robotic ultrasound from clinical complaints.
arxiv:2604.25646 v2 · 2026-04-28 · cs.CV · cs.RO
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Claims
In real-robot experiments, SAMe achieved overall organ-hit rates of 97.3% for liver initialization and 81.7% for kidney initialization across the evaluated target sets. Even when restricted to the centroid target, SAMe outperformed the surface-heuristic baseline for both liver and kidney initialization.
That a single external body image is sufficient to instantiate an accurate, patient-specific anatomical representation for the grounded targets without any additional registration using preoperative CT or MRI.
SAMe grounds clinical complaints to target organs, builds a patient-specific anatomical map from a single external image, and outputs probe initialization poses, reaching 97.3% liver and 81.7% kidney hit rates on a real robot.
Receipt and verification
| First computed | 2026-05-20T01:05:14.507400Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8c16d065ebbe53c81a6e9633ce74785543f9081ff22cfb7db50735620100ddda
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/RQLNAZPLXZJ4QGTOSYZ445DYKV \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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