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SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images

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2 Published in 2022 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22802

Abstract: The segmentation of Organs At Risk (OAR) in Computed Tomography (CT) images is an essential part of the planning phase of radiation treatment to avoid the adverse effects of cancer radiotherapy treatment. Accurate segmentation is… read more here.

Keywords: self supervised; segmentation; organ segmentation; head neck ... See more keywords

Variance-aware attention U-Net for multi-organ segmentation.

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0 Published in 2021 at "Medical physics"

DOI: 10.1002/mp.15322

Abstract: PURPOSE With the continuous development of deep learning based medical image segmentation technology, it is expected to attain more robust and accurate performance for more challenging tasks, such as multi-organs, small/irregular areas, and ambiguous boundary issues.… read more here.

Keywords: segmentation; attention; variance; multi organ ... See more keywords

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

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0 Published in 2019 at "Medical image analysis"

DOI: 10.1016/j.media.2019.04.005

Abstract: Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the… read more here.

Keywords: segmentation; statistical fusion; attention; multi organ ... See more keywords

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.

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0 Published in 2021 at "Radiologic clinics of North America"

DOI: 10.1016/j.rcl.2021.07.001

Abstract: Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms… read more here.

Keywords: clinical artificial; intelligence applications; radiology; artificial intelligence ... See more keywords

Fully Automated Organ Segmentation in Male Pelvic CT Images

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1 Published in 2018 at "Physics in medicine and biology"

DOI: 10.1088/1361-6560/aaf11c

Abstract: Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists… read more here.

Keywords: male pelvic; segmentation; fully automated; organ segmentation ... See more keywords

Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation

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1 Published in 2020 at "IEEE Transactions on Medical Imaging"

DOI: 10.1109/tmi.2020.2975347

Abstract: Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by… read more here.

Keywords: organ segmentation; cascaded net; multi organ; model ... See more keywords

ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods

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1 Published in 2020 at "Plant Methods"

DOI: 10.1186/s13007-020-00573-w

Abstract: Background The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced… read more here.

Keywords: annotated data; plant; segmentation methods; organ segmentation ... See more keywords

Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs

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1 Published in 2022 at "IEEE transactions on medical imaging"

DOI: 10.48550/arxiv.2208.12428

Abstract: Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a… read more here.

Keywords: neural odes; segmentation; organ segmentation; robust prototypical ... See more keywords