Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction

Fang, Xi; Yan, Pingkun

doi:10.1109/tmi.2020.3001036

Cited by 121 publications

(51 citation statements)

References 47 publications

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“…The tuned model was then applied to segment all the chest CT volumes. Segmentation of pulmonary opacities was completed by our previously proposed method, Pyramid Input Pyramid Output Feature Abstraction Network (PIPO-FAN) [3] with publicly released source code. 2 The network integrates image pyramid and multi-scale feature analysis into one single end-to-end framework.…”

Section: Deep Learning-based Image Segmentationmentioning

confidence: 99%

Association of AI quantified COVID-19 chest CT and patient outcome

et al. 2021

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Purpose Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. Methods We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). Results AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, p < 0.001). Using AI-based scores produced significantly higher ( p < 0.05) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. Conclusions Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

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Section: Deep Learning-based Image Segmentationmentioning

confidence: 99%

Association of AI quantified COVID-19 chest CT and patient outcome

et al. 2021

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“…Multi-scale information can provide rich semantic features for medical image segmentation. In the past few years, many methods [4,[12][13][14][15][16][17] proposed appling Multi-scale information to enhance contextual aggregation. We review several methods about Multiscale information extraction.…”

Section: A Multi-scale Information Extractionmentioning

confidence: 99%

“…In addition to applying skip connections, the dilation convolution [8] with different rate and pooling operation [11] is also used to capture Multi-scale information [12,14,17,20]. For instances, to gather Multi-scale information, [14] designed a pooling strategy with different-size pooling kernels for medical image segmentation.…”

Section: A Multi-scale Information Extractionmentioning

confidence: 99%

“…In addition to designing skip connections to fuse differentlevel semantic features, other state-of-the-art methods based on FCNs architecture have been proposed to capture Multi-scale context feature information of image via using dilated convolutions with different sampling rate [8][9][10] and pooling operations [11][12][13][14]. For example, [14] designed Residual Multikernel Pooling (RMP) strategy which has different-size pooling kernels to fuse multi-scale context feature information.…”

Section: Introductionmentioning

confidence: 99%

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MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation

et al. 2020

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Automatic assessing the location and extent of liver and liver tumor is critical for radiologists, diagnosis and the clinical process. In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation. Unlike the previous works which extract the context information of medical image via applying the multi-scale feature fusion, we propose a novel network named Multi-scale Attention Net (MA-Net) by introducing self-attention mechanism into our method to adaptively integrate local features with their global dependencies. The MA-Net can capture rich contextual dependencies based on the attention mechanism. We design two blocks: Position-wise Attention Block (PAB) and Multi-scale Fusion Attention Block (MFAB). The PAB is used to model the feature interdependencies in spatial dimensions, which capture the spatial dependencies between pixels in a global view. In addition, the MFAB is to capture the channel dependencies between any feature map by multi-scale semantic feature fusion. We evaluate our method on the dataset of MICCAI 2017 LiTS Challenge. The proposed method achieves better performance than other state-of-the-art methods. The Dice values of liver and tumors segmentation are 0.960±0.03 and 0.749±0.08 respectively.

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“…The authors present a "pyramid-input pyramid-output" [63] architecture that can be trained in a multi-scale and partially labeled scenario. In order to discriminate the features in differing scales, they designed an "adaptive weighting layer to fuse the outputs in an automatic fashion" [63] www.ijacsa.thesai.org…”

Section: ] Thoraxmentioning

confidence: 99%

Deep Learning Architectures and Techniques for Multi-organ Segmentation

Ogrean¹,

Dorobanțiu²,

Brad³

2021

IJACSA

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Deep learning architectures used for automatic multi-organ segmentation in the medical field have gained increased attention in the last years as the results and achievements outweighed the older techniques. Due to improvements in the computer hardware and the development of specialized network designs, deep learning segmentation presents exciting developments and opportunities also for future research. Therefore, we have compiled a review of the most interesting deep learning architectures applicable to medical multi-organ segmentation. We have summarized over 50 contributions, most of which are more recent than 3 years. The papers were grouped into three categories based on the architecture: "Convolutional Neural Networks" (CNNs), "Fully Convolutional Neural Networks" (FCNs) and hybrid architectures that combine more designsincluding "Generative Adversarial Networks" (GANs) or "Recurrent Neural Networks" (RNNs). Afterwards we present the most used multi-organ datasets, and we finalize by making a general discussion of current shortcomings and future potential research paths.

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Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction

Cited by 121 publications

References 47 publications

Association of AI quantified COVID-19 chest CT and patient outcome

Association of AI quantified COVID-19 chest CT and patient outcome

MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation

Deep Learning Architectures and Techniques for Multi-organ Segmentation

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