Pith Number

pith:6U4WBOVO

pith:2026:6U4WBOVOMQZLFWH5A6Q6XZVS4W

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Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation

Franz Thaler, Martin Urschler, Michael Hudler

A standard 3D U-Net remains a strong baseline for cardiac CT segmentation while lightweight explicit shape priors deliver only marginal and often negative effects.

arxiv:2605.15707 v1 · 2026-05-15 · eess.IV · cs.CV

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1 Bitcoin timestamp

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

Across all experiments, a standard 3D U-Net surprisingly remained a very strong baseline, with handcrafted priors yielding at best marginal and inconsistent changes and often degrading performance.

C2weakest assumption

The tested implementations (shape-aware losses and spatial label distribution heatmap-guided U-Net variants) are representative of what lightweight explicit anatomical shape priors can achieve, and performance differences are attributable to the priors rather than implementation details or dataset specifics.

C3one line summary

Standard 3D U-Net remains a strong baseline for multi-compartment cardiac segmentation, with handcrafted shape priors yielding at best marginal or negative effects on MM-WHS CT and WHS++ datasets.

References

16 extracted · 16 resolved · 0 Pith anchors

[1] Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge.Medical Image Analysis, 58:101537, December 2019 2019

[2] Elena Zappon, Luca Azzolin, Matthias A F Gsell, Franz Thaler, Anton J Prassl, Robert Arnold, Karli Gillette, Mohammadreza Kariman, Martin Manninger, Daniel Scherr, Aurel Neic, Martin Urschler, Christo 2025

[3] Deep learning.Nature, 521(7553):436–444, May 2015 2015

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

[5] 3D U-net: Learning dense volumetric segmentation from sparse annotation 2016

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:01:13.683378Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f53960baae6432b2d8fd07a1ebe6b2e588b53e88709f52954ace82413218f6f0

Aliases

arxiv: 2605.15707 · arxiv_version: 2605.15707v1 · doi: 10.48550/arxiv.2605.15707 · pith_short_12: 6U4WBOVOMQZL · pith_short_16: 6U4WBOVOMQZLFWH5 · pith_short_8: 6U4WBOVO

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6U4WBOVOMQZLFWH5A6Q6XZVS4W \
  | 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: f53960baae6432b2d8fd07a1ebe6b2e588b53e88709f52954ace82413218f6f0

Canonical record JSON

{
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    "abstract_canon_sha256": "a68698131cd027e422f2689d1b128935458a371954c9804ee0b593d7c1b20234",
    "cross_cats_sorted": [
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    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-15T07:56:57Z",
    "title_canon_sha256": "9768c0bf60b994901a51afea30f0042efb3088f4308d5cd80ca9cedb7e8b3c6d"
  },
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  "source": {
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    "kind": "arxiv",
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}