Pith Number

pith:YMAZVBRD

pith:2026:YMAZVBRDPLGI47H5VK57JWCWVD

not attested not anchored not stored refs resolved

Statistical Hand Shape Modeling from Clinical CT Scans Using Deep Learning and Implicit Skinning

Deniz Karasahin, Gokce Guven, Hasan Fehmi Ates, Kaan Erdogan

A pipeline cleans CT scans with AI, aligns hands via bone-driven skinning and registration, then builds a PCA shape model validated against army survey data.

arxiv:2605.16980 v1 · 2026-05-16 · cs.CV

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\pithnumber{YMAZVBRDPLGI47H5VK57JWCWVD}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The resulting statistical shape distributions demonstrate strong agreement with the U.S. Army Anthropometric Survey (ANSUR II), supporting the anatomical validity of the reconstructed models.

C2weakest assumption

That the GBCPD++ non-rigid registration on skin surfaces produces accurate point-wise correspondence across the 90 selected subjects without introducing systematic distortions that would affect the subsequent PCA shape model.

C3one line summary

A deep learning pipeline cleans clinical CT scans of hands and produces a statistical shape model from 90 aligned meshes that shows strong agreement with U.S. Army anthropometric data.

References

26 extracted · 26 resolved · 0 Pith anchors

[1] Napier, "Hands", revised ed 1993

[2] Anthropometry of the Hand and Product Design, 2024 · doi:10.21608/idj.2024.378407

[3] Ergonomics and equipment design 1984

[4] U-Net: Convolutional networks for biomedical image segmentation, 2015

[5] Active shape models-their training and application, 1995

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:34.250040Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c3019a86237acc8e7cfdaabbf4d856a8e7e41f0237a4a6d84ebc1e8e5a83e1cc

Aliases

arxiv: 2605.16980 · arxiv_version: 2605.16980v1 · doi: 10.48550/arxiv.2605.16980 · pith_short_12: YMAZVBRDPLGI · pith_short_16: YMAZVBRDPLGI47H5 · pith_short_8: YMAZVBRD

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YMAZVBRDPLGI47H5VK57JWCWVD \
  | 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())"
# expect: c3019a86237acc8e7cfdaabbf4d856a8e7e41f0237a4a6d84ebc1e8e5a83e1cc

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4e0f28d304028fa2ee311df623ddfdcdd68ea648b2f89c271b2f131cc146ff30",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T13:00:34Z",
    "title_canon_sha256": "8c81537f8acd28597c4b61d471de7d57b961fe5025462bbaae5e342db64b09ca"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16980",
    "kind": "arxiv",
    "version": 1
  }
}