1 Ocak 2021 Dergi makalesi Açık Erişim

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

Citation Style Language JSON

{
  "DOI": "10.1038/s41598-021-91467-x", 
  "abstract": "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.", 
  "author": [
    {
      "family": "Alis", 
      "given": " Deniz"
    }, 
    {
      "family": "Yergin", 
      "given": " Mert"
    }, 
    {
      "family": "Alis", 
      "given": " Ceren"
    }, 
    {
      "family": "Topel", 
      "given": " Cagdas"
    }, 
    {
      "family": "Asmakutlu", 
      "given": " Ozan"
    }, 
    {
      "family": "Bagcilar", 
      "given": " Omer"
    }, 
    {
      "family": "Senli", 
      "given": " Yeseren Deniz"
    }, 
    {
      "family": "Ustundag", 
      "given": " Ahmet"
    }, 
    {
      "family": "Salt", 
      "given": " Vefa"
    }, 
    {
      "family": "Dogan", 
      "given": " Sebahat Nacar"
    }, 
    {
      "family": "Velioglu", 
      "given": " Murat"
    }, 
    {
      "family": "Selcuk", 
      "given": " Hakan Hatem"
    }, 
    {
      "family": "Kara", 
      "given": " Batuhan"
    }, 
    {
      "family": "Oksuz", 
      "given": " Ilkay"
    }, 
    {
      "family": "Kizilkilic", 
      "given": " Osman"
    }, 
    {
      "family": "Karaarslan", 
      "given": " Ercan"
    }
  ], 
  "container_title": "SCIENTIFIC REPORTS", 
  "id": "235848", 
  "issue": "1", 
  "issued": {
    "date-parts": [
      [
        2021, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study", 
  "type": "article-journal", 
  "volume": "11"
}