DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation

Computers in Biology and Medicine, Год журнала: 2023, Номер 154, С. 106626 - 106626

Опубликована: Фев. 1, 2023

Deep learning architecture with convolutional neural network achieves outstanding success in the field of computer vision. Where U-Net has made a great breakthrough biomedical image segmentation and been widely applied wide range practical scenarios. However, equal design every downsampling layer encoder part simply stacked convolutions do not allow to extract sufficient information features from different depths. The increasing complexity medical images brings new challenges existing methods. In this paper, we propose deeper more compact split-attention u-shape network, which efficiently utilises low-level high-level semantic based on two frameworks: primary feature conservation block. We evaluate proposed model CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018, SegPC-2021 BraTS-2021 datasets. As result, our displays better performance than other state-of-the-art methods terms mean intersection over union dice coefficient. More significantly, demonstrates excellent challenging images. code for work technical details can be found at https://github.com/xq141839/DCSAU-Net.

Язык: Английский

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Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

Medical Image Analysis, Год журнала: 2023, Номер 85, С. 102762 - 102762

Опубликована: Янв. 31, 2023

Язык: Английский

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Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Computers in Biology and Medicine, Год журнала: 2023, Номер 169, С. 107840 - 107840

Опубликована: Дек. 16, 2023

Язык: Английский

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A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions

Scientific Data, Год журнала: 2022, Номер 9(1)

Опубликована: Окт. 4, 2022

We describe a publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) 513 without PET-positive lesions (negative controls)) acquired between 2014 2018 were included. All examinations on single, state-of-the-art PET/CT scanner. The imaging protocol consisted whole-body FDG-PET acquisition corresponding diagnostic CT scan. FDG-avid identified as based the clinical report manually segmented PET images in slice-per-slice (3D) manner. provide anonymized original DICOM files all well segmentation masks. In addition, we scripts for image processing conversion to different file formats (NIfTI, mha, hdf5). Primary diagnosis, age sex are provided non-imaging information. demonstrate how this can be used deep learning-based automated analysis data trained learning model.

Язык: Английский

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Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge

Medical Image Analysis, Год журнала: 2022, Номер 82, С. 102616 - 102616

Опубликована: Сен. 13, 2022

Язык: Английский

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Lecture notes in computer science, Год журнала: 2022, Номер unknown, С. 1 - 37

Опубликована: Янв. 1, 2022

Язык: Английский

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Joint EANM/SNMMI guideline on radiomics in nuclear medicine

European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2022, Номер 50(2), С. 352 - 375

Опубликована: Ноя. 3, 2022

The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses both hand-crafted and deep learning-based approaches.

Язык: Английский

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Current and Emerging Trends in Medical Image Segmentation With Deep Learning

IEEE Transactions on Radiation and Plasma Medical Sciences, Год журнала: 2023, Номер 7(6), С. 545 - 569

Опубликована: Апрель 10, 2023

In recent years, the segmentation of anatomical or pathological structures using deep learning has experienced a widespread interest in medical image analysis. Remarkably successful performance been reported many imaging modalities and for variety clinical contexts to support clinicians computer-assisted diagnosis, therapy, surgical planning purposes. However, despite increasing amount challenges, there remains little consensus on which methodology performs best. Therefore, we examine this article numerous developments breakthroughs brought since rise U-Net-inspired architectures. Especially, focus technical challenges emerging trends that community is now focusing on, including conditional generative adversarial cascaded networks, Transformers, contrastive learning, knowledge distillation, active prior embedding, cross-modality multistructure analysis, federated semi-supervised self-supervised paradigms. We also suggest possible avenues be further investigated future research efforts.

Язык: Английский

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Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?

Diagnostics, Год журнала: 2023, Номер 13(4), С. 667 - 667

Опубликована: Фев. 10, 2023

Developers and users of artificial-intelligence-based tools for automatic contouring treatment planning in radiotherapy are expected to assess clinical acceptability these tools. However, what is ‘clinical acceptability’? Quantitative qualitative approaches have been used this ill-defined concept, all which advantages disadvantages or limitations. The approach chosen may depend on the goal study as well available resources. In paper, we discuss various aspects acceptability’ how they can move us toward a standard defining new autocontouring

Язык: Английский

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DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images

Patterns, Год журнала: 2024, Номер 5(3), С. 100929 - 100929

Опубликована: Фев. 8, 2024

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, DR grading. The scientific community responded positively to 11, 12, 13 teams submitting different solutions for these tasks, respectively. This paper presents concise summary analysis of top-performing results across all tasks. These could provide practical guidance developing accurate classification segmentation models assessment diagnosis using UW-OCTA images, potentially improving diagnostic capabilities healthcare professionals. has been released support development computer-aided systems evaluation.

Язык: Английский

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