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Pregled bibliografske jedinice broj: 1105438

A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver


Sitnik, Dario; Aralica, Gorana; Hadžija, Mirko; Popović Hadžija, Marijana; Pačić, Arijana; Milković Periša, Marija; Manojlović, Luka; Krstanaca, Karolina; Plavetić, Andrija; Kopriva, Ivica
A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver // Biomedical Signal Processing and Control, 66 (2021), 102402, 11 doi:10.1016/j.bspc.2020.102402 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1105438 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver

Autori
Sitnik, Dario ; Aralica, Gorana ; Hadžija, Mirko ; Popović Hadžija, Marijana ; Pačić, Arijana ; Milković Periša, Marija ; Manojlović, Luka ; Krstanaca, Karolina ; Plavetić, Andrija ; Kopriva, Ivica

Izvornik
Biomedical Signal Processing and Control (1746-8094) 66 (2021); 102402, 11

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
intraoperative diagnosis ; metastatic colon cancer ; liver ; stain normalization ; U-Net(++) ; DeepLabv3

Sažetak
The lack of pixel-wise annotated images severely hinders the deep learning approach to computer-aided diagnosis in histopathology. This research creates a public database comprised of: (i) a dataset of 82 histopathological images of hematoxylin-eosin stained frozen sections acquired intraoperatively on 19 patients diagnosed with metastatic colon cancer in a liver ; (ii) corresponding pixel-wise ground truth maps annotated by four pathologists, two residents in pathology, and one final-year student of medicine. The Fleiss' kappa equal to 0.74 indicates substantial inter-annotator agreement ; (iii) two datasets with images stain-normalized relative to two target images ; (iv) development of two conventional machine learning and three deep learning-based diagnostic models. The database is available at http://cocahis.irb.hr. For binary, cancer vs. non-cancer, pixel-wise diagnosis we develop: SVM, kNN, U-Net, U-Net++, and DeepLabv3 classifiers that combine results from original images and stain-normalized images, which can be interpreted as different views. On average, deep learning classifiers outperformed SVM and kNN classifiers on an independent test set 14% in terms of micro balanced accuracy, 15% in terms of the micro F1 score, and 26% in terms of micro precision. As opposed to that, the difference in performance between deep classifiers is within 2%. We found an insignificant difference in performance between deep classifiers trained from scratch and corresponding classifiers pre-trained on non-domain image datasets. The best micro balanced accuracy estimated on the independent test set by the U-Net++ classifier equals 89.34%. Corresponding amounts of F1 score and precision are, respectively, 83.67% and 81.11%.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Temeljne medicinske znanosti



POVEZANOST RADA


Projekti:
IP-2016-06-5235 - Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti (DEDAD) (Kopriva, Ivica, HRZZ - 2016-06) ( POIROT)

Ustanove:
Institut "Ruđer Bošković", Zagreb,
Medicinski fakultet, Zagreb,
Klinička bolnica "Dubrava",
Klinički bolnički centar Zagreb

Citiraj ovu publikaciju

Sitnik, Dario; Aralica, Gorana; Hadžija, Mirko; Popović Hadžija, Marijana; Pačić, Arijana; Milković Periša, Marija; Manojlović, Luka; Krstanaca, Karolina; Plavetić, Andrija; Kopriva, Ivica
A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver // Biomedical Signal Processing and Control, 66 (2021), 102402, 11 doi:10.1016/j.bspc.2020.102402 (međunarodna recenzija, članak, znanstveni)
Sitnik, D., Aralica, G., Hadžija, M., Popović Hadžija, M., Pačić, A., Milković Periša, M., Manojlović, L., Krstanaca, K., Plavetić, A. & Kopriva, I. (2021) A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver. Biomedical Signal Processing and Control, 66, 102402, 11 doi:10.1016/j.bspc.2020.102402.
@article{article, year = {2021}, pages = {11}, DOI = {10.1016/j.bspc.2020.102402}, chapter = {102402}, keywords = {intraoperative diagnosis, metastatic colon cancer, liver, stain normalization, U-Net(++), DeepLabv3}, journal = {Biomedical Signal Processing and Control}, doi = {10.1016/j.bspc.2020.102402}, volume = {66}, issn = {1746-8094}, title = {A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver}, keyword = {intraoperative diagnosis, metastatic colon cancer, liver, stain normalization, U-Net(++), DeepLabv3}, chapternumber = {102402} }
@article{article, year = {2021}, pages = {11}, DOI = {10.1016/j.bspc.2020.102402}, chapter = {102402}, keywords = {intraoperative diagnosis, metastatic colon cancer, liver, stain normalization, U-Net(++), DeepLabv3}, journal = {Biomedical Signal Processing and Control}, doi = {10.1016/j.bspc.2020.102402}, volume = {66}, issn = {1746-8094}, title = {A Dataset and a Methodology for Intraoperative Computer-Aided Diagnosis of a Metastatic Colon Cancer in a Liver}, keyword = {intraoperative diagnosis, metastatic colon cancer, liver, stain normalization, U-Net(++), DeepLabv3}, chapternumber = {102402} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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