CT Liver Segmentation via PVT-based Encoding and Refined Decoding
This work addresses the need for accurate liver segmentation in medical imaging to improve diagnosis and treatment planning, representing an incremental advance in domain-specific methods.
The paper tackled liver segmentation from CT scans by proposing PVTFormer, a deep learning model combining a pretrained pyramid vision transformer with refined decoding, achieving a dice coefficient of 86.78%, mIoU of 78.46%, and HD of 3.50 on the LiTS 2017 benchmark.
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning. Computer-aided diagnosis systems promise to improve the precision of liver disease diagnosis, disease progression, and treatment planning. In response to the need, we propose a novel deep learning approach, \textit{\textbf{PVTFormer}}, that is built upon a pretrained pyramid vision transformer (PVT v2) combined with advanced residual upsampling and decoder block. By integrating a refined feature channel approach with a hierarchical decoding strategy, PVTFormer generates high quality segmentation masks by enhancing semantic features. Rigorous evaluation of the proposed method on Liver Tumor Segmentation Benchmark (LiTS) 2017 demonstrates that our proposed architecture not only achieves a high dice coefficient of 86.78\%, mIoU of 78.46\%, but also obtains a low HD of 3.50. The results underscore PVTFormer's efficacy in setting a new benchmark for state-of-the-art liver segmentation methods. The source code of the proposed PVTFormer is available at \url{https://github.com/DebeshJha/PVTFormer}.