YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
Wen Sheng Zhong Zheng Jiajun Liu Han Lu Hanyuan Zhang Zhengyong Jiang Zhihong Zhang Daoping Zhu
Background: Liver tumors are abnormal growths in the liver that can be either
benign or malignant, with liver cancer being a significant health concern
worldwide. However, there is no dataset for plain scan segmentation of liver
tumors, nor any related algorithms. To fill this gap, we propose Plain Scan
Liver Tumors(PSLT) and YNetr. Methods: A collection of 40 liver tumor plain
scan segmentation datasets was assembled and annotated. Concurrently, we
utilized Dice coefficient as the metric for assessing the segmentation outcomes
produced by YNetr, having advantage of capturing different frequency
information. Results: The YNetr model achieved a Dice coefficient of 62.63% on
the PSLT dataset, surpassing the other publicly available model by an accuracy
margin of 1.22%. Comparative evaluations were conducted against a range of
models including UNet 3+, XNet, UNetr, Swin UNetr, Trans-BTS, COTr, nnUNetv2
(2D), nnUNetv2 (3D fullres), MedNext (2D) and MedNext(3D fullres). Conclusions:
We not only proposed a dataset named PSLT(Plain Scan Liver Tumors), but also
explored a structure called YNetr that utilizes wavelet transform to extract
different frequency information, which having the SOTA in PSLT by experiments.