Multi-Organ Abdominal Ct Reference Standard Segmentations

Gibson, Eli; Giganti, Francesco; Hu, Yipeng; Bonmati, Ester; Bandula, Steve; Gurusamy, Kurinchi; Davidson, Brian R; Pereira, Stephen P.; Clarkson, Matthew J.; Barratt, Dean C.

doi:10.5281/zenodo.1169361

Cited by 17 publications

(16 citation statements)

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“…However, per definition, fat cells within organs do not count as VAT and thus should be excluded from the final statistics. Public datasets like [5,6] already exist for multi-organ semantic segmentation and could be utilized to postprocess the segmentation results from this study by masking organs in the abdominal cavity.…”

Section: Discussionmentioning

confidence: 99%

Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

Koitka,

Kroll,

Malamutmann

et al. 2020

Preprint

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Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known hounsfield unit limits. The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Our results show that fully-automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analysing body composition in the clinical routine.

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Section: Discussionmentioning

confidence: 99%

Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

Koitka,

Kroll,

Malamutmann

et al. 2020

Preprint

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“…The pancreas-CT dataset [27], [43] comprised 82 abdominal contrast-enhanced 3D CT scans and was initially provided with manually drawn contours of the pancreas [44], [43]. Recently, 43 scans from this dataset have been re-annotated to include the segmentation of the liver, duodenum, stomach, esophagus, spleen, gallbladder, and left kidney [45]. Therefore, we use only 43 scans that have been re-annotated to incorporate labels for multiple organs.…”

Section: A Description Of Datasets and Data Preprocessingmentioning

confidence: 99%

Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks

2023

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Quantitative assessment of the abdominal region from CT scans requires the accurate delineation of abdominal organs. Therefore, automatic abdominal image segmentation has been the subject of intensive research for the past two decades. Recently, deep learning-based methods have resulted in stateof-the-art performance for the 3D abdominal CT segmentation. However, the complex characterization of abdominal organs with weak boundaries prevents the deep learning methods from accurate segmentation. Specifically, the voxels on the boundary of organs are more vulnerable to misprediction due to the highly-varying intensities. This paper proposes a method for improved abdominal image segmentation by leveraging organ-boundary prediction as a complementary task. We train 3D encoder-decoder networks to simultaneously segment the abdominal organs and their boundaries via multi-task learning. We explore two network topologies based on the extent of weights shared between the two tasks within a unified multi-task framework. In the first topology, the whole-organ prediction task and the boundary detection task share all the layers in the network except for the last task-specific layers. The second topology employs a single shared encoder but two separate task-specific decoders. The effectiveness of utilizing the organs' boundary information for abdominal multi-organ segmentation is evaluated on two publically available abdominal CT datasets: Pancreas-CT and the BTCV dataset. The improvements shown in segmentation results reveal the advantage of the multi-task training that forces the network to pay attention to ambiguous boundaries of organs. A maximum relative improvement of 3.5% and 3.6% is observed in Mean Dice Score for Pancreas-CT and BTCV datasets, respectively.

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“…[175] MS-TransUNet++ [178] 2022 MRI, CT prostate, liver liver tumor [107], prostate cancer [179] DSTUNet [180] 2022 MRI, CT abdominal, left ventricle, right ventricle, myocardium cardiac disease [113], colorectal cancer, ventral hernia [138], cardiac disease [181] SegTransVAE [182] 2022 CT, MRI kidney, brain kidney tumor [108], brain tumor [133] MT-UNet [ [113], [185], [186] ViTBIS [187] 2021 CT, MRI abdomen, brain brain tumor [123,122], colorectal cancer, ventral hernia [138] O-Net [88] 2022 dermoscopic, CT skin, abdomen melanoma [89], colorectal cancer, ventral hernia [138] connection part is a CNN-transformer-based encoder consisting of several convolutional multi-head attention blocks and multi-head attention blocks. The connection part takes the output of the encoder at different levels and its outputs are fed to the decoder.…”

Section: Segmentationmentioning

confidence: 99%

“…The mixed transformer module is also used to connect the encoder and decoder and is mainly composed of a Local-Global Gaussian-Weighted Self-Attention block and an external attention block. Xu and colleagues [184] proposed an ECT-NAS structure to segment CT and MRI images using three public datasets [185,186,113]. The proposed method is composed of several searching blocks consisting of CNN and a developed LiteTrans with two [202] parallel branches of convolution and local-global self-attention.…”

Section: Segmentationmentioning

confidence: 99%

Recent Progress in Transformer-based Medical Image Analysis

Liu¹,

Shen²

2022

Preprint

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The transformer has dominated the natural language processing (NLP) field for a long time. Recently, the transformer-based method has been adopted into the computer vision (CV) field and shows promising results. As an important branch of the CV field, medical image analysis joins the wave of the transformer-based method rightfully. In this review, we illustrate the principle of the attention mechanism, and the detailed structures of the transformer, and depict how the transformer is adopted into medical image analysis. We organize the transformer-based medical image analysis applications in a sequence of different tasks, including classification, segmentation, synthesis, registration, localization, detection, captioning, and denoising. For the mainstream classification and segmentation tasks, we further divided the corresponding works based on different medical imaging modalities. The datasets corresponding to the related works are also organized. We include thirteen modalities and more than twenty objects in our work.

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Multi-Organ Abdominal Ct Reference Standard Segmentations

Cited by 17 publications

References 0 publications

Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks

Recent Progress in Transformer-based Medical Image Analysis

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