Gland Instance Segmentation Using Deep Multichannel Neural Networks

Xu, Yan; Li, Yang; Wang, Yipei; Liu, Mingyuan; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

doi:10.1109/tbme.2017.2686418

Cited by 136 publications

(26 citation statements)

References 31 publications

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“…In the case of Hausdorff distance lower values are better; for other measures higher are better. The quantitative results are given in Table 9 which shows that our method produces competitive results compared to the state-of-the-art algorithms from the contest and ranks third after the recently proposed (Xu et al (2017); Manivannan et al (2018)) according the rank sum criteria set by the organisers. Xu et al (2017) and Manivannan et al (2018) in terms of qualitative and quantitative results.…”

Section: Gland Segmentation Challenge (Glas) Data Setmentioning

confidence: 99%

“…Another recently proposed multi-scale convolutional neural network (Song et al (2017)) trains the network at different scales of the Laplacian pyramid and merges the network in the upsampling path to perform segmentation. Xu et al (2016Xu et al ( , 2017 proposed a network that performs side supervision of boundary maps in addition to the foreground. Manivannan et al (2018) combined handcrafted features with deep learning for segmentation, but this approach is computationally expensive as it not only requires calculation of features using classical approaches but also a support vector machine (SVM) classifier to predict local label patches.…”

Section: Related Workmentioning

confidence: 99%

“…employ optimised handcrafted features in addition to deep learning Xu et al (2017). on the other hand is heavily optimised for gland segmentation.…”

mentioning

confidence: 99%

“…S and R in the table correspond to score and rank. It is important to note thatXu et al (2017) andManivannan et al (2018) employ optimised handcrafted features in addition to deep learning Xu et al (2017). on the other hand is optimised for gland segmentation.…”

mentioning

confidence: 99%

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Micro-Net: A unified model for segmentation of various objects in microscopy images

Raza

Cheung

Shaban

et al. 2019

Medical Image Analysis

207

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Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters.The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms.

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Section: Gland Segmentation Challenge (Glas) Data Setmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…employ optimised handcrafted features in addition to deep learning Xu et al (2017). on the other hand is heavily optimised for gland segmentation.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Micro-Net: A unified model for segmentation of various objects in microscopy images

Raza

Cheung

Shaban

et al. 2019

Medical Image Analysis

207

View full text Add to dashboard Cite

show abstract

“…Details of the neural network layers are described in Table 1. Loss function for single objective function For the base segmentation model, we used adaptive weighted cross-entropy [28] exploited the class weighting parameter to manage the imbalanced size of each class. We let ∈ ℝ denote a weight vector with elements > 0 defined over the range of class labels ∈ {1,2, .…”

Section: Multi Task Learning For Image Segmentationmentioning

confidence: 99%

X-ray Image Segmentation using Multi-task Learning

Park¹,

Jeong²,

Moon³

2020

KSII TIIS

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The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

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Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm

Roy,

Gupta,

Goswami

2024

Int J Imaging Syst Tech

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Colorectal adenocarcinoma, the most prevalent form of colon cancer, originates in the glandular structures of the intestines, presenting histopathological abnormalities in affected tissues. Accurate gland segmentation is crucial for identifying these potentially fatal abnormalities. While recent methodologies have shown success in segmenting glands in benign tissues, their efficacy diminishes when applied to malignant tissue segmentation. This study aims to develop a robust learning algorithm using a convolutional neural network (CNN) to segment glandular structures in colon histology images. The methodology employs a CNN based on the U‐Net architecture, augmented by a weighted ensemble network that integrates DenseNet 169, Inception V3, and Efficientnet B3 as backbone models. Additionally, the segmented gland boundaries are refined using the watershed algorithm. Evaluation on the Warwick‐QU dataset demonstrates promising results for the ensemble model, by achieving an F1 score of 0.928 and 0.913, object dice coefficient of 0.923 and 0.911, and Hausdorff distances of 38.97 and 33.76 on test sets A and B, respectively. These results are compared with outcomes from the GlaS challenge (MICCAI 2015) and existing research findings. Furthermore, our model is validated with a publicly available dataset named LC25000, and visual inspection reveals promising results, further validating the efficacy of our approach. The proposed ensemble methodology underscores the advantages of amalgamating diverse models, highlighting the potential of ensemble techniques to enhance segmentation tasks beyond individual model capabilities.

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Gland Instance Segmentation Using Deep Multichannel Neural Networks

Cited by 136 publications

References 31 publications

Micro-Net: A unified model for segmentation of various objects in microscopy images

Micro-Net: A unified model for segmentation of various objects in microscopy images

X-ray Image Segmentation using Multi-task Learning

Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm

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