Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions Article Swipe
Yitong Yang
,
Zahraw Shah
,
Athira Jacob
,
Jackson Hair
,
Teodora Chițiboi
,
Tiziano Passerini
,
Jérôme Yerly
,
Lorenzo Di Sopra
,
Davide Piccini
,
Zahra Hosseini
,
Puneet Sharma
,
Anurag Sahu
,
Matthias Stuber
,
John N. Oshinski
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fradi.2023.1144004
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fradi.2023.1144004
Introduction Deep learning (DL)-based segmentation has gained popularity for routine cardiac magnetic resonance (CMR) image analysis and in particular, delineation of left ventricular (LV) borders for LV volume determination. Free-breathing, self-navigated, whole-heart CMR exams provide high-resolution, isotropic coverage of the heart for assessment of cardiac anatomy including LV volume. The combination of whole-heart free-breathing CMR and DL-based LV segmentation has the potential to streamline the acquisition and analysis of clinical CMR exams. The purpose of this study was to compare the performance of a DL-based automatic LV segmentation network trained primarily on computed tomography (CT) images in two whole-heart CMR reconstruction methods: (1) an in-line respiratory motion-corrected (Mcorr) reconstruction and (2) an off-line, compressed sensing-based, multi-volume respiratory motion-resolved (Mres) reconstruction. Given that Mres images were shown to have greater image quality in previous studies than Mcorr images, we hypothesized that the LV volumes segmented from Mres images are closer to the manual expert-traced left ventricular endocardial border than the Mcorr images. Method This retrospective study used 15 patients who underwent clinically indicated 1.5 T CMR exams with a prototype ECG-gated 3D radial phyllotaxis balanced steady state free precession (bSSFP) sequence. For each reconstruction method, the absolute volume difference (AVD) of the automatically and manually segmented LV volumes was used as the primary quantity to investigate whether 3D DL-based LV segmentation generalized better on Mcorr or Mres 3D whole-heart images. Additionally, we assessed the 3D Dice similarity coefficient between the manual and automatic LV masks of each reconstructed 3D whole-heart image and the sharpness of the LV myocardium-blood pool interface. A two-tail paired Student’s t -test (alpha = 0.05) was used to test the significance in this study. Results & Discussion The AVD in the respiratory Mres reconstruction was lower than the AVD in the respiratory Mcorr reconstruction: 7.73 ± 6.54 ml vs. 20.0 ± 22.4 ml, respectively ( n = 15, p -value = 0.03). The 3D Dice coefficient between the DL-segmented masks and the manually segmented masks was higher for Mres images than for Mcorr images: 0.90 ± 0.02 vs. 0.87 ± 0.03 respectively, with a p -value = 0.02. Sharpness on Mres images was higher than on Mcorr images: 0.15 ± 0.05 vs. 0.12 ± 0.04, respectively, with a p -value of 0.014 ( n = 15). Conclusion We conclude that the DL-based 3D automatic LV segmentation network trained on CT images and fine-tuned on MR images generalized better on Mres images than on Mcorr images for quantifying LV volumes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fradi.2023.1144004
- https://www.frontiersin.org/articles/10.3389/fradi.2023.1144004/pdf
- OA Status
- gold
- Cited By
- 8
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379231763
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379231763Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fradi.2023.1144004Digital Object Identifier
- Title
-
Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-06-02Full publication date if available
- Authors
-
Yitong Yang, Zahraw Shah, Athira Jacob, Jackson Hair, Teodora Chițiboi, Tiziano Passerini, Jérôme Yerly, Lorenzo Di Sopra, Davide Piccini, Zahra Hosseini, Puneet Sharma, Anurag Sahu, Matthias Stuber, John N. OshinskiList of authors in order
- Landing page
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https://doi.org/10.3389/fradi.2023.1144004Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fradi.2023.1144004/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fradi.2023.1144004/pdfDirect OA link when available
- Concepts
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Segmentation, Artificial intelligence, Magnetic resonance imaging, Medicine, Computer vision, Computer science, Nuclear medicine, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(bSSFP) | 188 |
| abstract_inverted_index.Results | 277 |
| abstract_inverted_index.anatomy | 45 |
| abstract_inverted_index.between | 237, 319 |
| abstract_inverted_index.borders | 24 |
| abstract_inverted_index.cardiac | 10, 44 |
| abstract_inverted_index.compare | 79 |
| abstract_inverted_index.greater | 128 |
| abstract_inverted_index.images, | 136 |
| abstract_inverted_index.images. | 160, 228 |
| abstract_inverted_index.images: | 336, 360 |
| abstract_inverted_index.in-line | 104 |
| abstract_inverted_index.method, | 193 |
| abstract_inverted_index.network | 88, 389 |
| abstract_inverted_index.primary | 211 |
| abstract_inverted_index.provide | 34 |
| abstract_inverted_index.purpose | 73 |
| abstract_inverted_index.quality | 130 |
| abstract_inverted_index.routine | 9 |
| abstract_inverted_index.studies | 133 |
| abstract_inverted_index.trained | 89, 390 |
| abstract_inverted_index.volume. | 48 |
| abstract_inverted_index.volumes | 142, 206 |
| abstract_inverted_index.whether | 215 |
| abstract_inverted_index.DL-based | 56, 84, 217, 384 |
| abstract_inverted_index.absolute | 195 |
| abstract_inverted_index.analysis | 15, 67 |
| abstract_inverted_index.assessed | 231 |
| abstract_inverted_index.balanced | 183 |
| abstract_inverted_index.clinical | 69 |
| abstract_inverted_index.computed | 92 |
| abstract_inverted_index.conclude | 381 |
| abstract_inverted_index.coverage | 37 |
| abstract_inverted_index.learning | 2 |
| abstract_inverted_index.magnetic | 11 |
| abstract_inverted_index.manually | 203, 325 |
| abstract_inverted_index.methods: | 101 |
| abstract_inverted_index.patients | 167 |
| abstract_inverted_index.previous | 132 |
| abstract_inverted_index.quantity | 212 |
| abstract_inverted_index.two-tail | 260 |
| abstract_inverted_index.volumes. | 411 |
| abstract_inverted_index.& | 278 |
| abstract_inverted_index.ECG-gated | 179 |
| abstract_inverted_index.Sharpness | 351 |
| abstract_inverted_index.automatic | 85, 241, 386 |
| abstract_inverted_index.including | 46 |
| abstract_inverted_index.indicated | 171 |
| abstract_inverted_index.isotropic | 36 |
| abstract_inverted_index.off-line, | 112 |
| abstract_inverted_index.potential | 61 |
| abstract_inverted_index.primarily | 90 |
| abstract_inverted_index.prototype | 178 |
| abstract_inverted_index.resonance | 12 |
| abstract_inverted_index.segmented | 143, 204, 326 |
| abstract_inverted_index.sequence. | 189 |
| abstract_inverted_index.sharpness | 252 |
| abstract_inverted_index.underwent | 169 |
| abstract_inverted_index.(DL)-based | 3 |
| abstract_inverted_index.Conclusion | 379 |
| abstract_inverted_index.Discussion | 279 |
| abstract_inverted_index.assessment | 42 |
| abstract_inverted_index.clinically | 170 |
| abstract_inverted_index.compressed | 113 |
| abstract_inverted_index.difference | 197 |
| abstract_inverted_index.fine-tuned | 395 |
| abstract_inverted_index.interface. | 258 |
| abstract_inverted_index.popularity | 7 |
| abstract_inverted_index.precession | 187 |
| abstract_inverted_index.similarity | 235 |
| abstract_inverted_index.streamline | 63 |
| abstract_inverted_index.tomography | 93 |
| abstract_inverted_index.Student’s | 262 |
| abstract_inverted_index.acquisition | 65 |
| abstract_inverted_index.coefficient | 236, 318 |
| abstract_inverted_index.combination | 50 |
| abstract_inverted_index.delineation | 19 |
| abstract_inverted_index.endocardial | 155 |
| abstract_inverted_index.generalized | 220, 399 |
| abstract_inverted_index.investigate | 214 |
| abstract_inverted_index.particular, | 18 |
| abstract_inverted_index.performance | 81 |
| abstract_inverted_index.phyllotaxis | 182 |
| abstract_inverted_index.quantifying | 409 |
| abstract_inverted_index.respiratory | 105, 116, 284, 294 |
| abstract_inverted_index.ventricular | 22, 154 |
| abstract_inverted_index.whole-heart | 31, 52, 98, 227, 248 |
| abstract_inverted_index.DL-segmented | 321 |
| abstract_inverted_index.Introduction | 0 |
| abstract_inverted_index.hypothesized | 138 |
| abstract_inverted_index.multi-volume | 115 |
| abstract_inverted_index.respectively | 306 |
| abstract_inverted_index.segmentation | 4, 58, 87, 219, 388 |
| abstract_inverted_index.significance | 273 |
| abstract_inverted_index.Additionally, | 229 |
| abstract_inverted_index.automatically | 201 |
| abstract_inverted_index.expert-traced | 152 |
| abstract_inverted_index.reconstructed | 246 |
| abstract_inverted_index.respectively, | 344, 368 |
| abstract_inverted_index.retrospective | 163 |
| abstract_inverted_index.determination. | 28 |
| abstract_inverted_index.free-breathing | 53 |
| abstract_inverted_index.reconstruction | 100, 108, 192, 286 |
| abstract_inverted_index.sensing-based, | 114 |
| abstract_inverted_index.Free-breathing, | 29 |
| abstract_inverted_index.motion-resolved | 117 |
| abstract_inverted_index.reconstruction. | 119 |
| abstract_inverted_index.reconstruction: | 296 |
| abstract_inverted_index.self-navigated, | 30 |
| abstract_inverted_index.high-resolution, | 35 |
| abstract_inverted_index.motion-corrected | 106 |
| abstract_inverted_index.myocardium-blood | 256 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5033876516 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I130701444, https://openalex.org/I150468666, https://openalex.org/I2802612298 |
| citation_normalized_percentile.value | 0.87271921 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |