A region-based convolutional network for nuclei detection and segmentation in microscopy images

Liang, Huixin; Cheng, Zhiming; Zhong, Haiqin; Qu, Ang; Chen, Lingna

doi:10.1016/j.bspc.2021.103276

Cited by 19 publications

(2 citation statements)

References 46 publications

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“…In [35], the Mask R-CNN was used to, accurately, detect the candidate ROIs of nuclei. Hao Liang et al [36] integrated the Guided Anchoring (GA) with the Region Proposal Network (RPN) to implement a GA-RPN module that generates candidate proposals for nuclei detection, then the Mask R-CNN was applied on the extracted ROI, for nuclear instance segmentation. A fast and accurate region-based nuclei instance segmentation algorithm was presented by Cheng et al [37].…”

Section: Related Workmentioning

confidence: 99%

Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Ahmed

2024

J Real-Time Image Proc

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Automated nucleus segmentation is considered the gold standard for diagnosing some severe diseases. Accurate instance segmentation of nuclei is still very challenging because of the large number of clustered nuclei, and the different appearance of nuclei for different tissue types. In this paper, a neural network is proposed for fast and accurate instance segmentation of nuclei in histopathology images. The network is inspired by the Unet and residual nets. The main contribution of the proposed model is enhancing the classification accuracy of nuclear boundaries by moderately preserving the spatial features by relatively d the size of feature maps. Then, a proposed 2D convolution layer is used instead of the conventional 3D convolution layer, the core of CNN-based architectures, where the feature maps are first compacted before being convolved by 2D kernel filters. This significantly reduces the processing time and avoids the out of memory problem of the GPU. Also, more features are extracted when getting deeper into the network without degrading the spatial features dramatically. Hence, the number of layers, required to compensate the loss of spatial features, is reduced that also reduces the processing time. The proposed approach is applied to two multi-organ datasets and evaluated by the Aggregated Jaccard Index (AJI), F1-score and the number of frames per second. Also, the formula of AJI is modified to reflect the object- and pixel-level errors more accurately. The proposed model is compared to some state-of-the-art architectures, and it shows better performance in terms of the segmentation speed and accuracy.

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Section: Related Workmentioning

confidence: 99%

Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Ahmed

2024

J Real-Time Image Proc

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“…Image segmentation is the first step of image analysis which separates an image into several regions with similar properties. Several segmentation approaches have been suggested in the literature, such as edge-based [ 15 , 19 , 26 , 44 , 47 ], region-based [ 12 , 13 , 22 , 41 , 46 ], deep-learning [ 21 , 38 ], and histogram thresholding [ 8 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

Ehsaeyan

2023

Multimed Tools Appl

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Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.

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ConvNext Mixer‐Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images

Firat,

Üzen,

Hanbay

et al. 2024

Int J Imaging Syst Tech

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Histopathology, vital in diagnosing medical conditions, especially in cancer research, relies on analyzing histopathology images (HIs). Nuclei segmentation, a key task, involves precisely identifying cell nuclei boundaries. Manual segmentation by pathologists is time‐consuming, prompting the need for robust automated methods. Challenges in segmentation arise from HI complexities, necessitating advanced techniques. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed nuclei segmentation. This study emphasizes feature extraction, introducing the ConvNext Mixer‐based Encoder‐Decoder (CNM‐ED) model. Unlike traditional CNN based models, the proposed CNM‐ED model enables the extraction of spatial and long context features to address the inherent complexities of histopathology images. This method leverages a multi‐path strategy using a traditional CNN architecture as well as different paths focused on obtaining customized long context features using the ConvNext Mixer block structure that combines ConvMixer and ConvNext blocks. The fusion of these diverse features in the final segmentation output enables improved accuracy and performance, surpassing existing state‐of‐the‐art segmentation models. Moreover, our multi‐level feature extraction strategy is more effective than models using self‐attention mechanisms such as SwinUnet and TransUnet, which have been frequently used in recent years. Experimental studies were conducted using five different datasets (TNBC, MoNuSeg, CoNSeP, CPM17, and CryoNuSeg) to analyze the performance of the proposed CNM‐ED model. Comparisons were made with various CNN based models in the literature using evaluation metrics such as accuracy, AJI, macro F1 score, macro intersection over union, macro precision, and macro recall. It was observed that the proposed CNM‐ED model achieved highly successful results across all metrics. Through comparisons with state‐art‐of models from the literature, the proposed CNM‐ED model stands out as a promising advancement in nuclei segmentation, addressing the intricacies of histopathological images. The model demonstrates enhanced diagnostic capabilities and holds the potential for significant progress in medical research.

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A region-based convolutional network for nuclei detection and segmentation in microscopy images

Cited by 19 publications

References 46 publications

Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

ConvNext Mixer‐Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images

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