Affiliations: [a] Laboratory of Signal Image and Energy Mastery (SIME), LR13ES03, University of Tunis, ENSIT, 1008, Tunis, Tunisia | [b] Laboratory of Biophysics and Medical Technologies, LR13ES07, University of Tunis EL Manar, ISTMT, 1006, Tunis, Tunisia
Correspondence:
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Corresponding author: Amine Ben Slama, Laboratory of Biophysics and Medical Technologies, LR13ES07, University of Tunis EL Manar, ISTMT, 1006, Tunis, Tunisia. Tel.: +216 97 96 8181; E-mail: amineslama1@gmail.com.
Abstract: This study proposes a new predictive segmentation method for liver tumors detection using computed tomography (CT) liver images. In the medical imaging field, the exact localization of metastasis lesions after acquisition faces persistent problems both for diagnostic aid and treatment effectiveness. Therefore, the improvement in the diagnostic process is substantially crucial in order to increase the success chance of the management and the therapeutic follow-up. The proposed procedure highlights a computerized approach based on an encoder–decoder structure in order to provide volumetric analysis of pathologic tumors. Specifically, we developed an automatic algorithm for the liver tumors defect segmentation through the Seg-Net and U-Net architectures from metastasis CT images. In this study, we collected a dataset of 200 pathologically confirmed metastasis cancer cases. A total of 8,297 CT image slices of these cases were used developing and optimizing the proposed segmentation architecture. The model was trained and validated using 170 and 30 cases or 85% and 15% of the CT image data, respectively. Study results demonstrate the strength of the proposed approach that reveals the superlative segmentation performance as evaluated using following indices including F1-score = 0.9573, Recall = 0.9520, IOU = 0.9654, Binary cross entropy = 0.0032 and p-value <0.05, respectively. In comparison to state-of-the-art techniques, the proposed method yields a higher precision rate by specifying metastasis tumor position.
Keywords: Liver tumors, CT images, segmentation, deep transfer learning, encoder-decoder architecture
