Evidence map›Paper›PMID 37541456›Full record

ArticleMagnetic resonance imaging2023

Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages.

Thomas Lilieholm, Alan McMillan, Azam Ahmed, Matthew Henningsen, Matthew Larson, Walter F Block

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. 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

6 authors.

Thomas LilieholmDepartment of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA. Electronic address: tlilieholm@wisc.edu.
Alan McMillanDepartment of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
Azam AhmedDepartment of Neurosurgery, University of Wisconsin at Madison, Madison, WI, USA.
Matthew HenningsenDepartment of Electrical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
Matthew LarsonDepartment of Radiology, University of Wisconsin at Madison, Madison, WI, USA.
Walter F BlockDepartment of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.

Funding

Biotechnology Training ProgramT32GM135066 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI SCOTT M. COYLE, BRIAN G FOX · 2020 to 2026
$7.0M
Improved Techniques for Substitute CT Generation from MRI datasetsR01EB026708 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI MCMILLAN, ALAN BLAIR · 2018 to 2021
$1.8M
Can machines be trusted? Robustification of deep learning for medical imagingR01LM013151 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI GARRETT, JOHN WILLIAM, MCMILLAN, ALAN BLAIR · 2020 to 2023
$1.3M
NIBIB NIH HHS R01 EB026708NIGMS NIH HHS T32 GM135066NLM NIH HHS R01 LM013151
6 · The paper itself

Abstract

introductionMinimally-invasive surgical techniques for intracerebral hemorrhage (ICH) evacuation use imaging to guide the suction, lysing and/or drainage from the hemorrhage site via various designs. A previous international surgical study has shown that reduction of hematoma volume below 15 ml is indicative of improved long term patient outcomes. The study noted a need for tools to periodically visualize remaining clot during intervention to increase the likelihood of evacuating sufficient clot volumes without endangering rebleeds. Robust segmentation of MRI could guide surgeons and radiologists regarding remaining regions and approaches for prudent evacuation. We thus propose a Convolutional Neural Network (CNN) to identify and autonomously segment clot and peripheral edema in MR images of the brain and generate an estimate of the remaining clot volume. MATERIALS AND

methodsWe used a retrospective, locally-acquired dataset of ICH patient scans taken on 3 T MRI scanners. Three sets of ground truth manual segmentations were independently generated by two imaging scientists and one radiology fellow. Evaluation of clot age was determined based on relative contrast of hemorrhage components and reviewed by a neurosurgeon. Model accuracy was determined by pixel-wise Dice coefficient (DC) calculations between each ground truth manual segmentation and the machine-derived autonomous segmentations.

resultsThe model produced autonomous segmentations of clot core with an average DC of 0.75 ± 0.21 relative to manual segmentations of the same scans. For edema, it produced segmentations with an average DC of 0.68 ± 0.16 relative to manual. From these pixel-wise segmentations, clot volume can be calculated. Model-produced segmentations underestimated clot volumes by an average of 17% relative to ground-truth.

conclusionThe machine learning models were able to identify and segment volumes of ICH components swiftly and accurately.

Indexed as

Cerebral HemorrhageNeural Networks, ComputerBrainEdemaHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingRetrospective StudiesImage segmentationInterventional toolsIntracerebral hemorrhageMachine learning

Identifiers

PMID37541456
PMCPMC10528387

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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.