Deep learning and level set approach for liver and tumor segmentation from CT scans

Abstract

Purpose Segmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time‐consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow‐up assessment. Method This work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region‐based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region‐based level aims to refine the predicted segmentation to find the correct final segmentation. Results The effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively. Conclusion The proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.

Artificial intelligence in precision medicine in hepatology

“…The best liver segmentation algorithm achieved a dice score around 0.96, whereas for tumor segmentation, the best algorithm evaluated around 0.70 24,25 . There are many pre‐processing or post‐processing methods, such as level setting and ML assistance to assist the accuracy of segmentation 24,26 . The accuracy of automatic liver segmentation or volumetry is acceptable with practical clinical application 27 .…”

Artificial intelligence in gastroenterology and hepatology: Status and challenges

“…For patients diagnosed with liver tumors or pancreatic cancer, it is crucial to complete the liver or pancreas segmentation to assess the lesions and make the ideal treatment plan. Instead of conventional manual segmentation, a CNNs model was proposed to segment liver tumors based on CT images, with an accuracy of more than 80.0%, favoring suitable decision-making[ 101 ]. Additionally, a CNNs model was also developed for pancreas localization and segmentation using CT images[ 102 ].…”

Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

“…We used Python 3.6 to implement the corresponding algorithms. Experiments were performed on the Codalab dataset ( https://competitions.codalab.org/ ) [ 48 , 49 ]. We employed 378 CT images for model training and the remaining sequences for testing.…”