| Title |
Pancreas segmentation in CT images: state of the art in clinical practice / |
| Authors |
Pocė, Ingrida ; Arsenjeva, Jaroslava ; Kielaitė-Gulla, Aistė ; Samuilis, Artūras ; Strupas, Kęstutis ; Dzemyda, Gintautas |
| DOI |
10.22364/bjmc.2021.9.1.02 |
| Full Text |
|
| Is Part of |
Baltic journal of modern computing.. Riga : University of Latvia. 2021, vol. 9, no. 1, p. 25-34.. ISSN 2255-8942. eISSN 2255-8950 |
| Keywords [eng] |
pancreatic segmentation ; pancreatic cancer ; deep learning ; artificial intelligence ; convolutional neural networks |
| Abstract [eng] |
Pancreas adenocarcinoma is a lethal diseasewith poor outcomes. With increasing incidence worldwide, it is predicted to become the second leading cause of cancer death in many countries. The main factor influencing disease outcome is the tumor stage at the time of diagnosis. The first step to successfully diagnose and treat pancreatic cancer is the efficient recognition and segmentation of the target organ. Several methods based on deep learning and data fusion for pancreas segmentation have been developed and applied over the years. This paper presents a state of the art in the application ofthe existing methods that have been presented for the pancreas and pancreatic cyst detection and segmentation.The most successful method so far has accuracy equal to 90.18% and AUC equal to 94.55%.Also,this paper looks into software designed for 3D segmentationthat is simple and potentially mightbe used by users from non-medical fields. |
| Published |
Riga : University of Latvia |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
