VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.

Anjany Sekuboyina, Malek E Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu

October 2021 Med Image Anal

Synopsis of Social media discussions

Discussions highlight the importance of the VerSe benchmark in advancing spine segmentation techniques, with remarks like 'crucial for improving automated spine analysis' and mentions of technological progress such as GPU-based reconstructions. The tone combines acknowledgment of scientific progress and interest in applying these advancements, reflecting a balanced view of the article's impact on the field.

A
Agreement
 Moderate agreement

Most discussions acknowledge the significance of the VerSe benchmark for spine segmentation research, indicating general agreement on its importance.

I
Interest
 Moderate level of interest

The tone reflects moderate curiosity with mentions of technological progress and new features, suggesting a shared interest but not high enthusiasm.

E
Engagement
 Moderate level of engagement

Participants reference various aspects of the publication, such as the challenges in vertebrae labelling and its implications, showing thoughtful engagement.

I
Impact
 Moderate level of impact

The collective tone conveys that the publication is considered an impactful contribution to medical imaging and AI research, although not revolutionary.

Social Mentions

YouTube

4 Videos

Twitter

13 Posts

Metrics

Video Views

6,735

Total Likes

64

Extended Reach

14,642

Social Features

17

Timeline: Posts about article

Top Social Media Posts

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    With ImFusion Spine Suite, we help you streamline your spine workflow - on CT and MRI. Contact us to give it a try! https://t.co/s0BFUyazu0
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    November 24, 2023

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    RT @ImFusionGmbH: We are thrilled to present our revamped GPU-based Cone-beam computed tomographic reconstruction. Watch our brand new vid…
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    We are thrilled to present our revamped GPU-based Cone-beam computed tomographic reconstruction. Watch our brand new video to discover more: https://t.co/KJyFWRGHg1 #ct #software #reconstruction
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    @arXiv_reaDer (Twitter)

    VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images VerSe：マルチ検出器CT画像の椎骨ラベリングおよびセグメンテーションベンチマーク 2022-04-05T08:17:55+00:00 arXiv: https://t.co/2pRlfRXysL 英/日サマリ↓ https://t.co/Iv46bjNvl0
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  • arXiv reaDer bot (cs-CV)
    @arXiv_reaDer (Twitter)

    VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images VerSe：マルチ検出器CT画像の椎骨ラベリングおよびセグメンテーションベンチマーク 2021-07-30T12:58:27+00:00 arXiv: https://t.co/TzLjFNnBhI 英/日サマリ↓ https://t.co/70mfAgN8x7
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    August 1, 2021

  • Laurence Court, Ph.D.
    @LaurenceECourt (Twitter)

    RT @nethertonians: VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images is now published! https://t.co/O…
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    July 30, 2021

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  • Tucker Netherton, PhD, DMP
    @nethertonians (Twitter)

    VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images is now published! https://t.co/OIGjaFlqFe https://t.co/4T07AhcUmV
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    July 30, 2021

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    @arXiv_reaDer (Twitter)

    VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images VerSe：マルチ検出器CT画像の椎骨ラベリングおよびセグメンテーションベンチマーク 2021-03-22T16:58:59+00:00 arXiv: https://t.co/H7tPedQxK0 英/日サマリ↓ https://t.co/rZvmcdZjEm
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    March 22, 2021

  • arXiv reaDer bot (cs-CV)
    @arXiv_reaDer (Twitter)

    VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images VerSe：マルチ検出器CT画像の椎骨ラベリングおよびセグメンテーションベンチマーク 2020-12-17T10:36:03+00:00 arXiv: https://t.co/HCDW3nqICx 英/日サマリ↓ https://t.co/TUks62T7v1
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Abstract Synopsis

  • Vertebral labelling and segmentation are crucial for improving automated spine image processing, aiding in clinical decision-making and population health analysis.
  • The Large Scale Vertebrae Segmentation Challenge (VerSe) was created to tackle the challenges of this field by having participants develop algorithms for labelling and segmenting vertebrae using a curated dataset of CT scans.
  • Results showed that an algorithm's performance depends significantly on its ability to identify vertebrae with rare anatomical variations, highlighting the complexities in spine analysis.