A Lightweight Deep Learning Approach for Liver Segmentation

Smaranda Bogoi () and Andreea Udrea
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Smaranda Bogoi: Department of Automatic Control and Systems Engineering, Faculty of Computer Science and Automatic Control, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Andreea Udrea: Department of Automatic Control and Systems Engineering, Faculty of Computer Science and Automatic Control, University “Politehnica” of Bucharest, 060042 Bucharest, Romania

Mathematics, 2022, vol. 11, issue 1, 1-20

Abstract: Liver segmentation is a prerequisite for various hepatic interventions and is a time-consuming manual task performed by radiology experts. Recently, various computationally expensive deep learning architectures tackled this aspect without considering the resource limitations of a real-life clinical setup. In this paper, we investigated the capabilities of a lightweight model, UNeXt, in comparison with the U-Net model. Moreover, we conduct a broad analysis at the micro and macro levels of these architectures by using two training loss functions: soft dice loss and unified focal loss, and by substituting the commonly used ReLU activation function, with the novel Funnel activation function. An automatic post-processing step that increases the overall performance of the models is also proposed. Model training and evaluation were performed on a public database—LiTS. The results show that the UNeXt model (Funnel activation, soft dice loss, post-processing step) achieved a 0.9902 dice similarity coefficient on the whole CT volumes in the test set, with 15× fewer parameters in nearly 4× less inference time, compared to its counterpart, U-Net. Thus, lightweight models can become the new standard in medical segmentation, and when implemented thoroughly can alleviate the computational burden while preserving the capabilities of a parameter-heavy architecture.

Keywords: CT liver segmentation; lightweight neural network; LiTS; automatic post-processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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