Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning (2024)

Zoetmulder, R., Konduri, P. R., Obdeijn, I. V., Gavves, E., Išgum, I., Majoie, C. B. L. M., Dippel, D. W. J., Roos, Y. B. W. E. M., Goyal, M., Mitchell, P. J., Campbell, B. C. V., Lopes, D. K., Reimann, G., Jovin, T. G., Saver, J. L., Muir, K. W., White, P., Bracard, S., Chen, B., ... Marquering, H. A. (2021). Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning. Diagnostics, 11(9), Article 1621. https://doi.org/10.3390/diagnostics11091621

Zoetmulder, Riaan ; Konduri, Praneeta R. ; Obdeijn, Iris V. et al. / Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning. In: Diagnostics. 2021 ; Vol. 11, No. 9.

@article{8c1c74b522b441468af375cdeedcd3a2,

title = "Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning",

abstract = "Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convo-lutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies.",

author = "Riaan Zoetmulder and Konduri, {Praneeta R.} and Obdeijn, {Iris V.} and Efstratios Gavves and Ivana I{\v s}gum and Majoie, {Charles B.L.M.} and Dippel, {Diederik W.J.} and Roos, {Yvo B.W.E.M.} and Mayank Goyal and Mitchell, {Peter J.} and Campbell, {Bruce C.V.} and Lopes, {Demetrius K.} and Gernot Reimann and Jovin, {Tudor G.} and Saver, {Jeffrey L.} and Muir, {Keith W.} and Phil White and Serge Bracard and Bailiang Chen and Scott Brown and Schonewille, {Wouter J.} and {van der Hoeven}, Erik and Volker Puetz and Marquering, {Henk A.}",

note = "Acknowledgments: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 GPU used for this research. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = sep,

day = "4",

doi = "10.3390/diagnostics11091621",

language = "English",

volume = "11",

number = "9",

}

Zoetmulder, R, Konduri, PR, Obdeijn, IV, Gavves, E, Išgum, I, Majoie, CBLM, Dippel, DWJ, Roos, YBWEM, Goyal, M, Mitchell, PJ, Campbell, BCV, Lopes, DK, Reimann, G, Jovin, TG, Saver, JL, Muir, KW, White, P, Bracard, S, Chen, B, Brown, S, Schonewille, WJ, van der Hoeven, E, Puetz, V & Marquering, HA 2021, 'Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning', Diagnostics, vol. 11, no. 9, 1621. https://doi.org/10.3390/diagnostics11091621

Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning. / Zoetmulder, Riaan; Konduri, Praneeta R.; Obdeijn, Iris V. et al.
In: Diagnostics, Vol. 11, No. 9, 1621, 04.09.2021.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

AU - Zoetmulder, Riaan

AU - Konduri, Praneeta R.

AU - Obdeijn, Iris V.

AU - Gavves, Efstratios

AU - Išgum, Ivana

AU - Majoie, Charles B.L.M.

AU - Dippel, Diederik W.J.

AU - Roos, Yvo B.W.E.M.

AU - Goyal, Mayank

AU - Mitchell, Peter J.

AU - Campbell, Bruce C.V.

AU - Lopes, Demetrius K.

AU - Reimann, Gernot

AU - Jovin, Tudor G.

AU - Saver, Jeffrey L.

AU - Muir, Keith W.

AU - White, Phil

AU - Bracard, Serge

AU - Chen, Bailiang

AU - Brown, Scott

AU - Schonewille, Wouter J.

AU - van der Hoeven, Erik

AU - Puetz, Volker

AU - Marquering, Henk A.

N1 - Acknowledgments: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 GPU used for this research.Publisher Copyright:© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/9/4

Y1 - 2021/9/4

N2 - Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convo-lutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies.

AB - Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convo-lutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies.

UR - http://www.scopus.com/inward/record.url?scp=85114608266&partnerID=8YFLogxK

U2 - 10.3390/diagnostics11091621

DO - 10.3390/diagnostics11091621

M3 - Article

AN - SCOPUS:85114608266

VL - 11

JO - Diagnostics

JF - Diagnostics

IS - 9

M1 - 1621

ER -

Zoetmulder R, Konduri PR, Obdeijn IV, Gavves E, Išgum I, Majoie CBLM et al. Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning. Diagnostics. 2021 Sept 4;11(9):1621. doi: 10.3390/diagnostics11091621