Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images

Chanchal, Amit Kumar; Kumar, Aman; Lal, Shyam; Kini, Jyoti R.

doi:10.1016/j.compeleceng.2021.107177

Cited by 31 publications

(14 citation statements)

References 17 publications

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“…Thus enhancing the model and thereby enabling it to be applied further to other deep learning-based segmentation tasks. The measured values of the MoNuSeg Dataset are: AJI = 0.621, Dice = 0.813, RQ = 0.781, SQ = 0.762, and PQ = 0.596 for the same organ test, and AJI = 0.641, Dice = 0.837, RQ = 0.760, SQ = 0.775 and PQ = 0.592 for the different organ test Limitations: Some histopathological images still pose a problem for the segmentation of overlapping nuclei, and the SQ results for different organ tests aren't as good as the DIST model Year: 2021 Chanchal et al ( 2021b ) proposed Separable Convolutional Pyramid Pooling Network (SCPP-Net) for segmentation of kidney and breast histopathology images Features: Backbone: Visual Geometric Group (VGG) Loss: Binary Cross-entropy (BCE) Adam optimizer was employed, with the number of parameters being 5,088,955. The proposed unit emphasized two significant facts: keeping the kernel size fixed and increasing the corresponding fields by varying the four dilation rates; and reducing the trainable parameters by means of depth-wise separable convolution Comparison: U-Net, SegNet, Attention U-Net, DIST and Atrous Spatial Pyramid Pooling U-Net (ASPP U-Net) Datasets: Cell images of two distinct organs (Kidney and Breast) were taken from Haematoxylin and Eosin (H&E)-stained Triple Negative Breast Cancer (TNBC) Dataset (Naylor et al 2018 ), H&E-stained Kidney Dataset (Irshad et al 2015 ) and Multiple Organs Multi-Disease Histopathology Dataset (Kumar et al 2017 ), 2020 ) Parameters: F1-Score and Aggregated Jaccard Index (AJI) Inference: The proposed model was better in terms of performance as compared to other models, and in doing so, two significant flaws were overcome by the proposed model, i.e., separating nuclei from complex, structured histopathology images with varying histology and molecular characteristics and reducing the computational complexity and total trainable parameters.…”

Section: Survey On Deep Learning Based Nucleus Segmentationmentioning

confidence: 99%

“…Chanchal et al ( 2021b ) proposed Separable Convolutional Pyramid Pooling Network (SCPP-Net) for segmentation of kidney and breast histopathology images…”

Section: Survey On Deep Learning Based Nucleus Segmentationmentioning

confidence: 99%

“…This SCPP-Net model by Chanchal et al ( 2021b ) was built upon the idea of mining supplementary information at an advanced level, as depicted in Fig. 8 .…”

Section: Experimental Cnn Modelsmentioning

confidence: 99%

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A survey on recent trends in deep learning for nucleus segmentation from histopathology images

et al. 2023

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Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.

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Section: Survey On Deep Learning Based Nucleus Segmentationmentioning

confidence: 99%

“…Chanchal et al ( 2021b ) proposed Separable Convolutional Pyramid Pooling Network (SCPP-Net) for segmentation of kidney and breast histopathology images…”

Section: Survey On Deep Learning Based Nucleus Segmentationmentioning

confidence: 99%

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A survey on recent trends in deep learning for nucleus segmentation from histopathology images

et al. 2023

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“…Their study concluded that the performance of such a wide network is better than the deep and thin network. Chanchal A K et al and Aatresh A A et al in [ 2 , 6 ], used separable convolution pyramid pooling and dimension-wise pyramid pooling for nuclei segmentation tasks. A summary of state-of-the-art DL techniques useful for biomedical image segmentation is presented in Table 1 .…”

Section: Related Workmentioning

confidence: 99%

“…Binary Cross-Entropy is a combination of sigmoid activation and Cross-Entropy, which is discussed by Chanchal et al in [ 6 ]. we have a countable set of symbols X= { x 1 , x 2 ......... x i , x n }.…”

Section: Training and Implementationmentioning

confidence: 99%

Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

Chanchal

Lal

Kini

2022

Multimed Tools Appl

Self Cite

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To improve the process of diagnosis and treatment of cancer disease, automatic segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology images is the first step in digital pathology. The proposed deep structured residual encoder-decoder network (DSREDN) focuses on two aspects: first, it effectively utilized residual connections throughout the network and provides a wide and deep encoder-decoder path, which results to capture relevant context and more localized features. Second, vanished boundary of detected nuclei is addressed by proposing an efficient loss function that better train our proposed model and reduces the false prediction which is undesirable especially in healthcare applications. The proposed architecture experimented on three different publicly available H&E stained histopathological datasets namely: (I) Kidney (RCC) (II) Triple Negative Breast Cancer (TNBC) (III) MoNuSeg-2018. We have considered F1-score, Aggregated Jaccard Index (AJI), the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation. The evaluated score of nuclei segmentation indicated that the proposed architecture achieved a considerable margin over five state-of-the-art deep learning models on three different histopathology datasets. Visual segmentation results show that the proposed DSREDN model accurately segment the nuclear regions than those of the 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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Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images

Cited by 31 publications

References 17 publications

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

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

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