Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

• Published in: Scientific reports 2021-06, Vol.11 (1), p.12434-12434, Article 12434
• Main Authors: Alis, Deniz, Yergin, Mert, Alis, Ceren, Topel, Cagdas, Asmakutlu, Ozan, Bagcilar, Omer, Senli, Yeseren Deniz, Ustundag, Ahmet, Salt, Vefa, Dogan, Sebahat Nacar, Velioglu, Murat, Selcuk, Hakan Hatem, Kara, Batuhan, Oksuz, Ilkay, Kizilkilic, Osman, Karaarslan, Ercan
• Format: Article
• Language: English
• Subjects: 639/705/117; 639/705/794; 692/617/375/534; 692/700/1421/1628; 692/700/1421/65; Datasets; Deep learning; Humanities and Social Sciences; Ischemia; Lesions; Magnetic resonance imaging; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Segmentation; Transfer learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-91467-x

There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist’s performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.