Segmentation label propagation using deep convolutional neural networks and dense conditional random field

Gao, Mingchen; Xu, Ziyue; Lü, Le; Wu, Aaron; Nogues, Isabella; Summers, Ronald M.; Mollura, Daniel J.

doi:10.1109/isbi.2016.7493497

Cited by 45 publications

(26 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interstitial lung disease Anthimopoulos et al (2016) Classification of 2D patches into interstitial lung texture classes using a standard CNN Christodoulidis et al (2017) 2D interstitial pattern classification with CNNs pre-trained with a variety of texture data sets Gao et al (2016c) Propagates manually drawn segmentations using CNN and CRF for more accurate interstitial lung disease reference Gao et al (2016a) AlexNet applied to large parts of 2D CT slices to detect presence of interstitial patterns Gao et al (2016b) Uses regression to predict area covered in 2D slice with a particular interstitial pattern Tarando et al (2016) Combines existing computer-aided diagnosis system and CNN to classify lung texture patterns. van Tulder and de Bruijne (2016) Classification of lung texture and airways using an optimal set of filters derived from DBNs and RBMs Other applications Tajbakhsh et al (2015a) Multi-stream CNN to detect pulmonary embolism from candidates obtained from a tobogganing algorithm Carneiro et al (2016) Predicts 5-year mortality from thick slice CT scans and segmentation masks de Vos et al (2016a) Identifies the slice of interest and determine the distance between CT slices…”

Section: Eyementioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

10,074

5,414

View full text Add to dashboard Cite

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

show abstract

Section: Eyementioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

10,074

5,414

View full text Add to dashboard Cite

show abstract

“…However, medical imaging datasets are much smaller which hinders the training of convolutional neural networks, due to over-fitting. Nevertheless, deep learning-based methods have been applied to medical imaging for segmentation Gao et al (2016b), detection Navab et al (2015), and classification Gao et al (2016a) tasks by utilizing problem-specific modifications. For example, to predict the pixels in the border region in biomedical image segmentation, the missing context is extrapolated by mirroring the input image Ronneberger et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

Zhu

Harowicz

Zhang

et al. 2019

Computers in Biology and Medicine

View full text Add to dashboard Cite

Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. Material and Methods: In this institutional review board-approved study, we analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences of 131 patients at our institution with a core needle biopsy-confirmed diagnosis of DCIS. The patients had no preoperative therapy before breast MRI and no prior history of breast cancer. We explored two different deep learning approaches to predict whether there was a hidden (occult) invasive component in the analyzed tumors that was ultimately detected at surgical excision. In the first approach, we adopted the transfer learning strategy, in which a network pre-trained on a large dataset of natural images is fine-tuned with our DCIS images. Specifically, we used the GoogleNet model pre-trained on the ImageNet dataset. In the second approach, we used a pre-trained network to extract deep features, and a support vector machine (SVM) that utilizes these features to predict the upstaging of the DCIS. We used 10-fold cross validation and the area under the ROC curve (AUC) to estimate the performance of the predictive models. Results: The best classification performance was obtained using the deep features approach with GoogleNet model pre-trained on ImageNet as the feature extractor and a polynomial kernel SVM used as the classifier (AUC = 0.70, 95% CI: 0.58-0.79). For the transfer learning based approach, the highest AUC obtained was 0.53 (95% CI: 0.41-0.62). Conclusion: Convolutional neural networks could potentially be used to identify occult invasive disease in patients diagnosed with DCIS at the initial core needle biopsy.

show abstract

“…The most similar studies [6,11] targeting the classification of ILD patterns with patch-based CNNs achieved 85.5% and 92.8% classification accuracy, respectively. Binary classification of ILD, non-ILD and healthy (normal) subjects (15 in each group) was performed in [12] using linear regression and texture features.…”

Section: Resultsmentioning

confidence: 95%

“…This should be equivalent to the calculation of the metrics in 3D. For comparison with others, we report results for each architecture individually to approximate the performance of the methods in [6] and [11] by the patch-based CNN (Table 1, 1st row) and [10] by the CED (Table 1, 2nd row). Moreover, in order to assess the importance of information fusion, we also retrained the CED without using the probability maps from the patch-based network (Table 1, row 3) using the same 150 slices as in the proposed scheme (Table 1, row 4).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Deep patch-based priors under a fully convolutional encoder-decoder architecture for interstitial lung disease segmentation

Vakalopoulou

Chassagnon

Paragios

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

View full text Add to dashboard Cite

Interstitial lung diseases (ILD) encompass a large spectrum of diseases sharing similarities in their physiopathology and computed tomography (CT) appearance. In this paper, we propose the adaption of a deep convolutional encoder-decoder (CED) that has shown high accuracy for image segmentation. Such architectures require annotation of the total region with pathological findings. This is difficult to acquire, due to uncertainty in the definition and extent of disease patterns and the need of significant human effort, especially for large datasets. Therefore, often current methods use patch-based implementations of convolutional neural networks, which however tend to produce spatially inhomogeneous segmentations due to their local contextual view. We exploit the advantages of both architectures by using the output of a patch-based classifier as a prior to a CED. Our method could advance the state-of-the-art in lung tissue segmentation using only a small number of newly annotated images.

show abstract

Segmentation label propagation using deep convolutional neural networks and dense conditional random field

Cited by 45 publications

References 6 publications

A survey on deep learning in medical image analysis

A survey on deep learning in medical image analysis

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

Deep patch-based priors under a fully convolutional encoder-decoder architecture for interstitial lung disease segmentation

Contact Info

Product

Resources

About