Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks

Liao, Fei-Yu; Chen, Xi; Hu, Xiaolin; Song, Sen

doi:10.1109/tcyb.2017.2778799

Cited by 40 publications

(21 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As is reported in [23], registration takes at least 20 seconds per patient using GPUs and typically tens of minutes per patient using CPUs. Learning based RoI localization decouples RoI localization from prior knowledge [24][25][26][27][28]. Some of the related practices [24,29] ex-tract region proposals using external modules such as Selective Search [30] or Multiscale Combinatorial Grouping (MCG) [31], which are also well-known speed bottlenecks as is pointed out in [32] and replacing them with RPN accelerated a network from 0.5 fps to 5 fps.…”

Section: Introductionmentioning

confidence: 99%

3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Huang

Dou

Wang

et al. 2021

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Segmentation of colorectal cancerous regions from 3DMagnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localizationsegmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available.3D CNN, region of interest, multi-task learning, tumor segmentation, colorectal cancer.

show abstract

Section: Introductionmentioning

confidence: 99%

3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Huang

Dou

Wang

et al. 2021

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…However, in order to increase the robustness and accuracy, most of the mathematical segmentation requires a prior knowledge, problematic assumptions, or user interaction [13][14][15]. e representative methods consist of CNN [4,5,17], sliding window [18][19][20], Full Connected Network [6,[21][22][23], U-Net [7,8,24], and 3D Convolution [25][26][27].…”

Section: Segmentation Based On Mathematical Calculationsmentioning

confidence: 99%

“…In recent years, Cardiac Magnetic Resonance (CMR) has become a crucial imaging modality in clinical cardiology practice due to its high signal-to-noise ratio, noninvasive imaging to cardiac chambers, no need for geometric assumptions, and great vessels [1,2]. Although much effort has been devoted to left ventricle quantification over the last several decades [2][3][4][5][6][7][8], it remains in the research stage and the reported algorithms are still not robust and flexible enough to support clinic practice due to the complexity of medical imaging. erefore, left ventricle quantification is still acknowledged as a challenge with much room for improvements in robustness, flexibility, and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Zhang

et al. 2021

Complexity

View full text Add to dashboard Cite

More and more research on left ventricle quantification skips segmentation due to its requirement of large amounts of pixel-by-pixel labels. In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit (GRU). After that, the attention mechanism is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi-indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2-norm is used to avoid model overfitting. Compared with the state-of-the-art (SOTA), our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed.

show abstract

“…It is shown in the paper that this configuration performed better than other simpler u-nets in terms of Dice. In their article Liao et al [137] detected the Region of Interest (ROI) containing LV chambers and then used hypercolumns FCN to segment LV in the ROI. The 2-D segmentation results were integrated across different images to estimate the volume.…”

Section: A Magnetic Resonance Imagingmentioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

140

View full text Add to dashboard Cite

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

show abstract

Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks

Cited by 40 publications

References 29 publications

3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention

Deep Learning in Cardiology

Contact Info

Product

Resources

About