Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields

Jacobs, Joseph G.; Panagiotaki, Eleftheria; Alexander, Daniel C.

doi:10.1007/978-3-319-10581-9_11

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…Broadly speaking, previous studies in the literature can be categorized into two classes: (1) pixel based methods. For this kind of method, various hand-crafted features including texture, color, morphological cues and Haar-like features were utilized to detect the glandular structure from histology images [11,38,13,36,37,28,23,32]; (2) structure based methods. Most of approaches in this category take advantage of prior knowledge about the glandular structure, such as graph based methods [2,20], glandular boundary delineation with geodesic distance transform [16], polar space random field model [18], stochastic polygons model [35], etc.…”

Section: Introductionmentioning

confidence: 99%

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

523

339

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The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating clustered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating. Our method won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams, surpassing all the other methods by a significant margin.

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Section: Introductionmentioning

confidence: 99%

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Chen

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

523

339

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“…CRFs achieved state-of-the-art results in the image segmentation tasks, both in the traditional benchmark [47], as well as in application to medical image analysis, such as in PCa segmentation [48], weakly supervised segmentation of PCa [49], GS grading [50] or PCa detection [51].…”

Section: Conditional Random Fields As Recurrent Neural Networkmentioning

confidence: 99%

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

et al. 2020

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Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.

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“…Previously, hand-crafted features containing morphological information are designed and traditional graph-based models are frequently used [20,28,35,13]. However, malignant subjects vary seriously in appearance and they are beyond the capacity of traditional methods.…”

Section: Biomedical Image Segmentationmentioning

confidence: 99%

MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation

Zhang¹,

Jin²,

Xu³

et al. 2018

Preprint

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Radiologist is "doctor's doctor", biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions in biomedical image segmentation applications. In this paper, based on U-Net, we propose MDUnet, a multiscale densely connected U-net for biomedical image segmentation. we propose three different multi-scale dense connections for U shaped architectures encoder, decoder and across them. The highlights of our architecture is directly fuses the neighboring different scale feature maps from both higher layers and lower layers to strengthen feature propagation in current layer. Which can largely improves the information flow encoder, decoder and across them. Multi-scale dense connections, which means containing shorter connections between layers close to the input and output, also makes much deeper U-net possible. We adopt the optimal model based on the experiment and propose a novel Multi-scale Dense U-Net (MDU-Net) architecture with quantization. Which reduce overfitting in MDU-Net for better accuracy. We evaluate our purpose model on the MICCAI 2015 Gland Segmentation dataset (GlaS). The three multi-scale dense connections improve Unet performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile the MDU-net with quantization achieves the superiority over U-Net performance by up to 3% on test A and 4.1% on test B.

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Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields

Cited by 14 publications

References 12 publications

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation

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