mlsanityData to 13 Oct 2026

Automated segmentation of intracranial hemorrhages from 3D CT

arXiv 2209.10648 ↗First posted 21 Sep 2022eess.IV cs.CVcited by 2

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Abstract (arXiv)

Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs. In this work, we describe our solution to INSTANCE 2022. We use a 2D segmentation network, SegResNet from MONAI, operating slice-wise without resampling. The final submission is an ensemble of 18 models. Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.721), and overall rank 2. It is implemented with Auto3DSeg.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 2 of its 6 references that are in the map. First posted on arXiv 21 Sep 2022.

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2 of 6 references.

References · 2

  1. 2016Identity Mappings in Deep Residual Networkscited by 1,962
  2. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 325

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challengecited by 25
  2. 2025MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal outputcited by 15