Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation

Zhao, Bingchao; Chen, Xin; Li, Zhi; Yu, Zhiwen; Yao, Su; Yan, Li; Wang, Yuqian; Liu, Zaiyi; Liang, Changhong; Han, Chu

doi:10.1016/j.media.2020.101786

Cited by 111 publications

(44 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CIA-Net [10] utilized additional contour supervision to obtain segmentation with more accurate edges of nucleus. Triple U-Net [11] designed the parallel feature aggregation network to fuse features from Hematoxylin and RGB images progressively. Thus it learned a more precise nuclei boundaries.…”

Section: A Bottom-up Nuclei Segmentation and Classificationmentioning

confidence: 99%

“…In general, on histology nuclei segmentation and classification, the present CNN based approaches can be categorized into either bottom-up or top-down methods. The bottom-up structure is adopted by most existing methods [7]- [11] which first generate high-resolution semantic segmentation masks and then group the pixels into an arbitrary number of object instances, as shown in Figure 1 (a). Relying on complicated pixel grouping post-processing to extract object instances, the performance of bottom-up approaches is highly dependent on segmentation results and grouping methods.…”

Section: Introductionmentioning

confidence: 99%

“…Due to their architecture limitation, some methods fail to perform classification including U-Net[35], CIA-Net[10] and Triple U-Net[11] and hence we leave their results as "-".…”

mentioning

confidence: 99%

See 2 more Smart Citations

PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild

Yao¹,

Huang²,

Sun³

et al. 2021

Preprint

View full text Add to dashboard Cite

Automatic nuclei segmentation and classification plays a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In this paper, we aim to build a reliable and robust method capable of dealing with data from the 'the clinical wild'. Specifically, we study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin (H&E) stained histopathology data, and evaluate our approach using the recent largest dataset: PanNuke. We address the detection and classification of each nuclei as a novel semantic keypoint estimation problem to determine the center point of each nuclei. Next, the corresponding class-agnostic masks for nuclei center points are obtained using dynamic instance segmentation. By decoupling two simultaneous challenging tasks, our method can benefit from class-aware detection and class-agnostic segmentation, thus leading to a significant performance boost. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types, delivering new benchmark results.

show abstract

Section: A Bottom-up Nuclei Segmentation and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild

Yao¹,

Huang²,

Sun³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This architecture experienced widespread adoption for biomedical semantic segmentation tasks, usually with small deviation from the original design ( Al-Kofahi et al, 2018 ; He et al, 2021 ; Kose et al, 2021 ). As an example, ex vivo cellular nucleus segmentation has been performed by combining three U-Net–like branches with custom layer blocks ( Zhao et al, 2020 ). Similarly, dense layer blocks and dense concatenation were employed to increase the architecture depth and combine features for fine detail reconstruction and localization in in vivo multiphoton microscopy images ( Cai et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images

Sarti

Parlani

Díaz-Gómez

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.

show abstract

“…A large number of approaches have been proposed to handle the above challenges [3]- [5], [7]- [9], [11], [12], [17], [20], [21]. For example, Chen et al [3] differentiate instances of nuclei according to their boundaries.…”

Section: Introductionmentioning

confidence: 99%

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Chen

Ding

Tao

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-toboundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidencebased Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-toboundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper will be released.

show abstract

Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation

Cited by 111 publications

References 18 publications

PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild

PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild

Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Contact Info

Product

Resources

About