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Baressi Šegota S, Lorencin I, Smolić K, Anđelić N, Markić D, Mrzljak V, Štifanić D, Musulin J, Španjol J, Car Z. Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach. BIOLOGY 2021; 10:biology10111134. [PMID: 34827126 PMCID: PMC8614660 DOI: 10.3390/biology10111134] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 01/11/2023]
Abstract
Simple Summary Bladder cancer is a common cancer of the urinary tract, characterized by high metastatic potential and recurrence. The research applies a transfer learning approach on CT images (frontal, axial, and saggital axes) for the purpose of semantic segmentation of areas affected by bladder cancer. A system consisting of AlexNet network for plane recognition, using transfer learning-based U-net networks for the segmentation task. Achieved results show that the proposed system has a high performance, suggesting possible use in clinical practice. Abstract Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.
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Affiliation(s)
- Sandi Baressi Šegota
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Ivan Lorencin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Klara Smolić
- Clinical Hospital Center Rijeka, Krešimirova 42, 51000 Rijeka, Croatia; (K.S.); (D.M.); (J.Š.)
| | - Nikola Anđelić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Dean Markić
- Clinical Hospital Center Rijeka, Krešimirova 42, 51000 Rijeka, Croatia; (K.S.); (D.M.); (J.Š.)
- Faculty of Medicine, Branchetta 20/1, University of Rijeka, 51000 Rijeka, Croatia
| | - Vedran Mrzljak
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
- Correspondence: ; Tel.: +385-51-651551
| | - Daniel Štifanić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Jelena Musulin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Josip Španjol
- Clinical Hospital Center Rijeka, Krešimirova 42, 51000 Rijeka, Croatia; (K.S.); (D.M.); (J.Š.)
- Faculty of Medicine, Branchetta 20/1, University of Rijeka, 51000 Rijeka, Croatia
| | - Zlatan Car
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
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