mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network

Quantitative measurement of hepatocellular carcinoma (HCC) on multi-phase contrast-enhanced magnetic resonance imaging (CEMRI) is one of the key processes for HCC treatment and prognosis. However, direct automated quantitative measurement using the CNN-based network a still challenging task due to: (1) The lack of ability for capturing long-range dependencies of multi-anatomy in the whole medical image; (2) The lack of mechanism for fusing and selecting multi-phase CEMRI information. In this study, we propose a multi-function Transformer regression network (mfTrans-Net) for HCC quantitative measurement. Specifically, we first design three parallel CNN-based encoders for multi-phase CEMRI feature extraction and dimension reducing. Next, the non-local Transformer makes our mfTrans-Net self-attention for capturing the long-range dependencies of multi-anatomy. At the same time, a phase-aware Transformer captures the relevance between multi-phase CEMRI for multi-phase CEMRI information fusion and selection. Lastly, we proposed a multi-level training strategy, which enables an enhanced loss function to improve the quantification task. The mfTrans-Net is validated on multi-phase CEMRI of 138 HCC subjects. Our mfTrans-Net achieves high performance of multi-index quantification that the mean absolute error of center point, max-diameter, circumference, and area is down to 2.35 mm, 2.38 mm, 8.28 mm, and 116.15 mm\(^2\), respectively. The results show that mfTrans-Net has great potential for small lesions quantification in medical images and clinical application value.