Evidence map›Paper›PMID 39328866›Full record

ArticleOtology & neurotology open2024

Automatic Segmentation of Heschl Gyrus and Planum Temporale by MRICloud.

Carlos A Perez-Heydrich, Dominic Padova, Kwame Kutten, Can Ceritoglu, Andreia Faria, J Tilak Ratnanather, Yuri Agrawal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Otology & neurotology open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Carlos A Perez-HeydrichDepartment of Otolaryngology - Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD.
Dominic PadovaDepartment of Otolaryngology - Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD.
Kwame KuttenDepartment of Biomedical Engineering, Johns Hopkins University, Center for Imaging Science and Institute for Computational Medicine, Baltimore, MD.
Can CeritogluDepartment of Biomedical Engineering, Johns Hopkins University, Center for Imaging Science and Institute for Computational Medicine, Baltimore, MD.
Andreia FariaDepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD.
J Tilak RatnanatherDepartment of Biomedical Engineering, Johns Hopkins University, Center for Imaging Science and Institute for Computational Medicine, Baltimore, MD.
Yuri AgrawalDepartment of Otolaryngology - Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study used a cloud-based program, MRICloud, which parcellates T1 MRI brain scans using a probabilistic classification based on manually labeled multi-atlas, to create a tool to segment Heschl gyrus (HG) and the planum temporale (PT). Methods: MRICloud is an online platform that can automatically segment structural MRIs into 287 labeled brain regions. A 31-brain multi-atlas was manually resegmented to include tags for the HG and PT. This modified atlas set with additional manually labeled regions of interest acted as a new multi-atlas set and was uploaded to MRICloud. This new method of automated segmentation of HG and PT was then compared to manual segmentation of HG and PT in MRIs of 10 healthy adults using Dice similarity coefficient (DSC), Hausdorff distance (HD), and intraclass correlation coefficient (ICC). Results: This multi-atlas set was uploaded to MRICloud for public use. When compared to reference manual segmentations of the HG and PT, there was an average DSC for HG and PT of 0.62 ± 0.07, HD of 8.10 ± 3.47 mm, and an ICC for these regions of 0.83 (0.68-0.91), consistent with an appropriate automatic segmentation accuracy. Conclusion: This multi-atlas can alleviate the manual segmentation effort and the difficulty in choosing an HG and PT anatomical definition. This protocol is limited by the morphology of the MRI scans needed to make the MRICloud atlas set. Future work will apply this multi-atlas to observe MRI changes in hearing-associated disorders.

Indexed as

Hearing LossOtology/NeurotologyRadiology

Identifiers

PMID39328866
PMCPMC11424062

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.