bibtext: @InProceedings{ Jia_Towards_MICCAI2024,
                   author = { Jiang, Yuncheng and Hu, Yiwen and Zhang, Zixun and Wei, Jun and Feng, Chun-Mei and Tang, Xuemei and Wan, Xiang and Liu, Yong and Cui, Shuguang and Li, Zhen },
                   title = { { Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the lack of a large-scale ERUS dataset with high-quality annotations hinders the development of automatic ultrasound diagnostics. In this paper, we collected and annotated the first benchmark dataset that covers diverse ERUS scenarios, \textit{i.e.} colorectal cancer segmentation, detection, and infiltration depth staging. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. Based on this dataset, we further introduce a benchmark model for colorectal cancer segmentation, named the \textbf{A}daptive \textbf{S}parse-context \textbf{TR}ansformer (\textbf{ASTR}). ASTR is designed based on three considerations: scanning mode discrepancy, temporal information, and low computational complexity. For generalizing to different scanning modes, the adaptive scanning-mode augmentation is proposed to convert between raw sector images and linear scan ones. For mining temporal information, the sparse-context transformer is incorporated to integrate inter-frame local and global features. For reducing computational complexity, the sparse-context block is introduced to extract contextual features from auxiliary frames. Finally, on the benchmark dataset, the proposed ASTR model achieves a $77.6\%$ Dice score in rectal cancer segmentation, largely outperforming previous state-of-the-art methods.
title: Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
authors: Jiang, Yuncheng Hu, Yiwen Zhang, Zixun Wei, Jun Feng, Chun-Mei Tang, Xuemei Wan, Xiang Liu, Yong Cui, Shuguang Li, Zhen