Evidence map›Paper›PMID 34870221›Full record

ArticleRadiology. Artificial intelligence2021

Octree Representation Improves Data Fidelity of Cardiac CT Images and Convolutional Neural Network Semantic Segmentation of Left Atrial and Ventricular Chambers.

Kunal Gupta, Nitesh Sekhar, Davis M Vigneault, Anderson R Scott, Brendan Colvert, Amanda Craine, Adhithi Raghavan, Francisco J Contijoch

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 46% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
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  4. 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

8 authors at 1 institution in 1 country.

Kunal GuptaDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).ORCID https://orcid.org/0000-0001-8628-2202
Nitesh SekharDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).
Davis M VigneaultDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).ORCID https://orcid.org/0000-0003-3798-9812
Anderson R ScottDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).
Brendan ColvertDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).ORCID https://orcid.org/0000-0002-4812-8228
Amanda CraineDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).ORCID https://orcid.org/0000-0002-0595-8061
Adhithi RaghavanDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).ORCID https://orcid.org/0000-0002-0102-7470
Francisco J ContijochDepartments of Computer Science Engineering (K.G., N.S.), Bioengineering (D.M.V., A.R.S., B.C., A.C., A.R., F.J.C.), and Radiology (F.J.C.), University of California, San Diego, 9500 Gilman Dr, MC 0412, La Jolla, CA 92093; and Department of Internal Medicine, Scripps Mercy Hospital, San Diego, Calif (D.M.V.).ORCID https://orcid.org/0000-0001-9616-3274
Scripps Mercy Hospital · US

Funding

Training In Cardiovascular Physiology & PharmacologyT32HL007444 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Ju Chen, Robert Scott Ross · 1985 to 2026
$11.1M
True 4DCT for Quantifying LV Dyssynchrony and Function for Targeting LV Lead Location in CRTR01HL144678 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MCVEIGH, ELLIOT R · 2019 to 2022
$3.3M
UCSD PRIDE Faculty Development Program in Cardiovascular SciencesR25HL145817 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Robert Scott Ross, Joann Trejo · 2019 to 2026
$3.3M
Comprehensive Assessment of Right Ventricular and Pulmonary Vascular Function via CT Imaging in Heart Failure PatientsK01HL143113 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CONTIJOCH, FRANCISCO J · 2018 to 2022
$872k
NHLBI NIH HHS K01 HL143113NHLBI NIH HHS R01 HL144678NHLBI NIH HHS R25 HL145817NHLBI NIH HHS T32 HL007444
6 · The paper itself

Abstract

purposeTo assess whether octree representation and octree-based convolutional neural networks (CNNs) improve segmentation accuracy of three-dimensional images. MATERIALS AND

methodsCardiac CT angiographic examinations from 100 patients (mean age, 67 years ± 17 [standard deviation]; 60 men) performed between June 2012 and June 2018 with semantic segmentations of the left ventricular (LV) and left atrial (LA) blood pools at the end-diastolic and end-systolic cardiac phases were retrospectively evaluated. Image quality (root mean square error [RMSE]) and segmentation fidelity (global Dice and border Dice coefficients) metrics of the octree representation were compared with spatial downsampling for a range of memory footprints. Fivefold cross-validation was used to train an octree-based CNN and CNNs with spatial downsampling at four levels of image compression or spatial downsampling. The semantic segmentation performance of octree-based CNN (OctNet) was compared with the performance of U-Nets with spatial downsampling.

resultsOctrees provided high image and segmentation fidelity (median RMSE, 1.34 HU; LV Dice coefficient, 0.970; LV border Dice coefficient, 0.843) with a reduced memory footprint (87.5% reduction). Spatial downsampling to the same memory footprint had lower data fidelity (median RMSE, 12.96 HU; LV Dice coefficient, 0.852; LV border Dice coefficient, 0.310). OctNet segmentation improved the border segmentation Dice coefficient (LV, 0.612; LA, 0.636) compared with the highest performance among U-Nets with spatial downsampling (Dice coefficients: LV, 0.579; LA, 0.592).

conclusionOctree-based representations can reduce the memory footprint and improve segmentation border accuracy.

Indexed as

CardiacConvolutional Neural Network (CNN)CTDeep Learning AlgorithmsMachine Learning AlgorithmsSegmentationSupervised Learning

Identifiers

PMID34870221
PMCPMC8637236
OpenAlexW3204839353

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.