Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images

Xie, Lipeng; Qi, Jin; Pan, Lili; Wali, Samad

doi:10.1016/j.neucom.2019.09.083

Cited by 58 publications

(42 citation statements)

References 28 publications

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“…Thus, Mask R-CNNs should be able to individuate and separate single cells [61] (implemented in the Python programming language https://github.com/hwejin23/histopathology_segmentation). The last methods exploit a specific architecture for nuclei segmentation called Deep Interval-Marker-Aware Network (DIMAN) [23] (implemented in the Python programming language https://github.com/appiek/ Nuclei_Segmentation_Experiments_Demo), which learns multi-scale feature maps that are stacked as a feature pyramid by skip connections. Unlike the other methods that predict just the foreground regions, this architecture predicts the foreground, the interval regions and the marker of nuclei simultaneously.…”

Section: Nuclei Segmentation Methodsmentioning

confidence: 99%

“…Even the watershed transformation [40] was used for nuclei segmentation, but mostly as a post-processing method for overlapping objects segmentation. It was used both in its classical version, coupled with the distance transform [41], and in its marker-controlled version, where the markers are extracted with a conditional erosion [23,42].…”

Section: Related Workmentioning

confidence: 99%

“…In these methods, the images are directly used to feed CNN to label pixels as nuclear (foreground) or non-nuclear (background). Even with CNN-based methods, some additional post-processing operations have been used to separate the overlapping or touching nuclei in the foreground regions by using the shape deformation method [19] or the watershed transformation [23,28,36]. Other approaches instead added a third class for the nuclei boundary to define a precise separation between background and foreground regions, especially between touching nuclei, avoiding the post-processing procedures.…”

Section: Related Workmentioning

confidence: 99%

“…The first row of Figure 2 shows an image example for each type of cancer present in the dataset. During our experiments, we used the same partitions already used in [23] that consist of a training set composed by 24 randomly selected images (six for each cancer type) and a testing set composed by the remaining images (two for each cancer type). BNS (available at https://peterjacknaylor.github.io/PeterJackNaylor.github.io/2017/01/15/Isbi/), presented by Naylor et al [28], contains 33 manually annotated images that were obtained by randomly cropping the patches with a fixed size of 512 × 512 from the original H&E-stained histopathology WSIs.…”

Section: Datasetsmentioning

confidence: 99%

“…However, despite the considerable effort spent so far by the research community, and the performance improvements achieved by current methods based on CNNs on benchmark datasets [22,23], cell nuclei segmentation remains a challenging task [24][25][26] due to chromatic stain variability, non-homogenous background, nuclear overlap and occlusion and differences in cell morphology and stain density [2,19,27]. This task is even more challenging in real applications where the specimens could belong to different tissue types.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

Putzu

Fumera

2020

Applied Sciences

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Cell nuclei segmentation is a challenging task, especially in real applications, when the target images significantly differ between them. This task is also challenging for methods based on convolutional neural networks (CNNs), which have recently boosted the performance of cell nuclei segmentation systems. However, when training data are scarce or not representative of deployment scenarios, they may suffer from overfitting to a different extent, and may hardly generalise to images that differ from the ones used for training. In this work, we focus on real-world, challenging application scenarios when no annotated images from a given dataset are available, or when few images (even unlabelled) of the same domain are available to perform domain adaptation. To simulate this scenario, we performed extensive cross-dataset experiments on several CNN-based state-of-the-art cell nuclei segmentation methods. Our results show that some of the existing CNN-based approaches are capable of generalising to target images which resemble the ones used for training. In contrast, their effectiveness considerably degrades when target and source significantly differ in colours and scale.

show abstract

Section: Nuclei Segmentation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

Putzu

Fumera

2020

Applied Sciences

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Robust nuclei segmentation with encoder‐decoder network from the histopathological images

Gour,

Jain,

Kumar

2024

Int J Imaging Syst Tech

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Nuclei segmentation is a prerequisite and an essential step in cancer detection and prognosis. Automatic nuclei segmentation from the histopathological images is challenging due to nuclear overlap, disease types, chromatic stain variability, and cytoplasmic morphology differences. Furthermore, it is demanding to develop a single accurate method for segmenting nuclei of different organs because of the diversity in nuclei size, shape, and appearance across the various organs. To address these challenges, we developed a robust Encoder‐Decoder network for nuclei segmentation from the multi‐organ histopathological images. In this approach, we utilize a pre‐trained EfficientNet‐B4 as an Encoder subnetwork and design a new Decoder subnetwork architecture. Additionally, we have applied morphological operation‐based post‐processing to improve the segmentation results. The performance of our approach has been evaluated on three public datasets, namely, Kumar, TNBC, and CPM‐17 datasets, which contain histopathological images of seven organs, one organ, and four organs, respectively. The proposed method achieved an aggregated Jacquard index of 0.636, 0.611, and 0.706 on Kumar, TNBC, and CPM‐17 datasets, respectively. Our proposed approach also shows superiority over the existing methods.

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High‐resolution full‐field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

Jain,

Chang,

Singh

et al. 2023

Journal of Biophotonics

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Histopathology for tumor margin assessment is time‐consuming and expensive. High‐resolution full‐field optical coherence tomography (FF‐OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF‐OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF‐OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection‐over‐union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF‐OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

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Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images

Cited by 58 publications

References 28 publications

An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

Robust nuclei segmentation with encoder‐decoder network from the histopathological images

High‐resolution full‐field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

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