Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification.

Abstract

It is essential to detect and segment liver tumors to guide treatment and track disease progression. To reduce the need for large annotated data sets, we present an end-to-end pipeline that uses self-supervised pretraining to improve segmentation and then classifies tumor types with a separate pretrained classifier applied to the segmented tumor regions. First, we pretrained the encoder of a transformer-based network using a self-supervised approach on unlabeled abdominal computed tomography images. Subsequently, we fine-tuned the segmentation network to segment the liver and tumors, and the tumor regions were classified using a pretrained convolutional neural network (Inception-v3 architecture) as intrahepatic cholangiocarcinoma (ICC), hepatocellular carcinoma (HCC), or colorectal liver metastases (CRLMs). We evaluated 459 images (155 HCC, 107 ICC, 197 CRLM). For external testing, we used an independent public data set (n = 40). Averaged across HCC, ICC, and CRLM, in comparison with a supervised baseline (no pretraining), self-supervised pretraining improved the liver Dice similarity coefficient (DSC) by 6.4 percentage points and reduced the 95th-percentile Hausdorff distance (HD₉₅) by 32.97 mm. For tumors, the DSC increased by 6.0 percentage points and the HD₉₅ decreased by 3.2 mm. Tumor type classification achieved AUC 0.98 (95% CI, 0.96 to 1.00) and accuracy 96% (95% CI, 92% to 99%). Segmentation performance on the external data was close to the internal cohort with tumor DSC 0.73, intersection over union (IoU) 0.60, and HD₉₅ 30.98 mm and liver DSC 0.91, IoU 0.83, and HD₉₅ 29.67 mm. The proposed self-supervised, end-to-end pipeline improves liver tumor segmentation and provides accurate tumor type classification, supporting reliable radiologic assessment, treatment planning, and improved prognostication for patients with liver cancer.