Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development

• URL: http://arxiv.org/abs/2408.10067v1
• Date: Mon, 19 Aug 2024 15:04:42 GMT
• Title: Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
• Authors: Yuncheng Jiang, Yiwen Hu, Zixun Zhang, Jun Wei, Chun-Mei Feng, Xuemei Tang, Xiang Wan, Yong Liu, Shuguang Cui, Zhen Li,
• Abstract summary: In this paper, we collect and annotated the first benchmark dataset that covers diverse ERUS scenarios. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. We introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR)
• Score: 59.74920439478643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, 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 Adaptive Sparse-context TRansformer (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.

Related papers

• CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation [8.728236864462302]
  Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up. These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer. To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum. We also propose a novel medical cancer lesion segmentation benchmark model named U-SAM. The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information.
  arXiv  Detail & Related papers  (2023-08-16T10:51:27Z)
• Revisiting Computer-Aided Tuberculosis Diagnosis [56.80999479735375]
  Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. We establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD.
  arXiv  Detail & Related papers  (2023-07-06T08:27:48Z)
• High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model [10.412300404240751]
  Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years. Deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the screening process. However, developing robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations.
  arXiv  Detail & Related papers  (2023-05-02T01:04:22Z)
• Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation [7.31341312596412]
  We propose a novel framework composed of ConvNeXt backbone and Multi Kernel Positional Embedding block. Our model achieves the Dice coefficient of 0.8818 and the IOU score of 0.8163 on the Kvasir-SEG dataset.
  arXiv  Detail & Related papers  (2023-01-17T03:12:57Z)
• PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis [0.22485007639406518]
  COVID-19 diagnosis can now be done efficiently using PCR tests, but this use case exemplifies the need for a methodology to overcome data variability issues. We propose a novel generative approach that aims at erasing the differences induced by e.g. the imaging technology while simultaneously introducing minimal changes to the CT scans.
  arXiv  Detail & Related papers  (2022-08-19T15:49:47Z)
• Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance [58.01014026139231]
  We propose a new large-scale tuberculosis (TB) chest X-ray dataset (TBX-Att) We establish an attribute-assisted weakly-supervised framework to classify and localize TB by leveraging the attribute information. The proposed model is evaluated on the TBX-Att dataset and will serve as a solid baseline for future research.
  arXiv  Detail & Related papers  (2022-07-01T07:50:35Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.