Nuclei Segmentation in Histopathological Images Using Two-Stage Learning

Kang, Qingbo; Lao, Qicheng; Fevens, Thomas

doi:10.1007/978-3-030-32239-7_78

Cited by 40 publications

(22 citation statements)

References 18 publications

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“…Our method outperformed present state-of-the-art methods on the two datasets (described in section "Datasets") in the integrity of the segmentation of a single nucleus and the segmentation accuracy, and especially in the segmentation of overlapped nuclei regions. We compared our method against several deep learning based methods listed in Table 1, such as FCN-8 (Long et al, 2015), Mask R-CNN (He et al, 2015), U-Net (Ronneberger et al, 2015), CNN3 (Kumar et al, 2017), DIST (Naylor et al, 2019), SUNets, U-Net (DLA), a two-stage U-net (Mahbod et al, 2019), and two-stage learning U-Net (DLA) (Kang et al, 2019). In order to make the comparison objectively, we followed the same training and testing set split criteria suggested by Kumar et al (2017).…”

Section: Resultsmentioning

confidence: 99%

“…The segmentation results of our algorithm are further illustrated in Figure 3, where we selected two examples of segmentation at random and compared them with the GT. We also applied our method on the TNBC dataset and compared the experimental results with other methods (Table 3)-DeconvNet (Noh et al, 2015), FCN-8 (Long et al, 2015), U-Net (Ronneberger et al, 2015), Ensemble method (Naylor et al, 2017), DIST (Naylor et al, 2019), and two-stage learning U-Net (DLA) method (Kang et al, 2019). Our method has the top AJI value (AJI = 0.621) but the second highest F1 score (F1 score = 0.806).…”

Section: Resultsmentioning

confidence: 99%

“…This result demonstrates that our method has a high generalization ability since these images vary from tissue types to cell types. The values of AJI and F1 score of other methods in Tables 1-3 are taken from published works (Kang et al, 2019;Naylor et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism

Kong

Genchev

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Nuclei segmentation is a fundamental but challenging task in histopathological image analysis. One of the main problems is the existence of overlapping regions which increases the difficulty of independent nuclei separation. In this study, to solve the segmentation of nuclei and overlapping regions, we introduce a nuclei segmentation method based on two-stage learning framework consisting of two connected Stacked U-Nets (SUNets). The proposed SUNets consists of four parallel backbone nets, which are merged by the attention generation model. In the first stage, a Stacked U-Net is utilized to predict pixel-wise segmentation of nuclei. The output binary map together with RGB values of the original images are concatenated as the input of the second stage of SUNets. Due to the sizable imbalance of overlapping and background regions, the first network is trained with cross-entropy loss, while the second network is trained with focal loss. We applied the method on two publicly available datasets and achieved stateof-the-art performance for nuclei segmentation-mean Aggregated Jaccard Index (AJI) results were 0.5965 and 0.6210, and F1 scores were 0.8247 and 0.8060, respectively; our method also segmented the overlapping regions between nuclei, with average AJI = 0.3254. The proposed two-stage learning framework outperforms many current segmentation methods, and the consistent good segmentation performance on images from different organs indicates the generalized adaptability of our approach.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism

Kong

Genchev

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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“…However, these connections are shallow and linear [28] . There have many previous works intend to tackle this issue [14] . Based on these works, in our approach we present a novel feature aggregation paradigm: dense deep layer aggregation to further strengthen the skip connection.…”

Section: Network Architecturementioning

confidence: 99%

“…Nevertheless, the skip connection is just pure copy and paste, therefore it is still linear and shallow [28] . In [14], the skip connection is extended by DLA. However, it eliminates the shortcut path for gradients propagation.…”

Section: Dense Deep Layer Aggregationmentioning

confidence: 99%

Multi-path Aggregation U-Net for Lung Segmentation in Chest Radiographs

Hou

Gong

2021

Preprint

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Lung segmentation from chest X-ray images is a fundamental and crucial step for computer-aid diagnosis (CAD) system. Although many techniques for this problem have been proposed, it still remains as a challenge. Recently, Fully Convolutional Networks (FCNs) especially U-Net has been hugely successful for many image segmentation tasks. In this paper, we propose a revised variant of U-Net, specifically, we design two main components. The first component is multi-path dilated convolutions with different dilation rate to extract multi-scale features. It was used to replace the basic convolutions used in the original U-Net. The second component is skip connections with dense deep layer aggregation to further aggregate features across different scales. We perform extensive experiments on three publicly available datasets (in total 951 images). Our proposed method outperforms many other segmentation methods and achieves state-of-the-art segmentation performance (Dice’s coefficient of 96.5%, 97.9% and 96.7% on the three datasets, respectively).

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A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

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Nuclei Segmentation in Histopathological Images Using Two-Stage Learning

Cited by 40 publications

References 18 publications

Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism

Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism

Multi-path Aggregation U-Net for Lung Segmentation in Chest Radiographs

A survey on artificial intelligence in histopathology image analysis

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