BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images

Oda, Hirotaka; Roth, Holger R.; Chiba, Kosuke; Sokolic, Jure; Kitasaka, Takayuki; Oda, Masahiro; Hinoki, Akinari; Uchida, Hajime; Schnabel, Julia A.; Mori, Kensaku

doi:10.1007/978-3-030-00934-2_26

Cited by 71 publications

(35 citation statements)

References 5 publications

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“…For example, Chen et al [2] proposed a deep contour-aware network (DCAN) for the task of instance segmentation that firstly harnesses the complementary information of contour and instances to separate the attached objects. In order to utilize contour-specific features to assist nuclei prediction, BES-Net [17] directly concatenates the output contour features with nuclei features in decoders. However, it only learns complementary information in nuclei branch but ignores the potentially reversed benefits from nuclei to contour, which is more essential since contour appearance is more complicated and has larger intra-variance than that of nuclei.…”

Section: Introductionmentioning

confidence: 99%

“…(5) PA-Net[14]: a modified path aggregation network by adding path augmentation in two independent decoders to enhance the instance segmentation performance. (6) BES-Net[17]: the original boundary-enhanced segmentation network which concatenated contour features with nuclei features to enhance learning in boundary region (7). CIA-Net w/o IAM : the proposed network architecture with two independent decoders for nuclei and contour prediction respectively, but without Information Aggregation Module in decoders (8).…”

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confidence: 99%

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CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

Zhou

Onder²,

Dou

et al. 2019

Lecture Notes in Computer Science

156

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Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmenting unseen organ nuclei. To address these issues, we propose a novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multilevel information aggregation module between two task-specific decoders. Rather than independent decoders, it leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task-specific features. Furthermore, we proposed a novel smooth truncated loss that modulates losses to reduce the perturbation from outliers. Consequently, the network can focus on learning from reliable and informative samples, which inherently improves the generalization capability. Experiments on the 2018 MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness of our proposed method, surpassing all the other 35 competitive teams by a significant margin.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

Zhou

Onder²,

Dou

et al. 2019

Lecture Notes in Computer Science

156

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show abstract

“…Moreover, we compare Han-Net with the novel methods proposed in previous studies. These methods include: DCAN ( Chen et al, 2017 ), BES-Net ( Oda et al, 2018 ), CIA-Net ( Zhou et al, 2019 ), Spa-Net ( Koohbanani et al, 2019 ), FullNet ( Qu et al, 2019 ). They achieved competitive segmentation performance in the MoNuSeg dataset, respectively.…”

Section: Experimental and Resultsmentioning

confidence: 99%

A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

He¹,

Zhang²,

Chen³

et al. 2021

Front. Mol. Biosci.

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Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.

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“…Results and comparative analysis Performance of the proposed model is compared against several deep learning based methods as reported in Table 1. Except the baseline method (CNN3) [7] which categories the image pixels into three classes using a CNN-based classifier, other methods in Table 1 (DR-Net [11], DCAN [1], BES-Net [12], and CIA-Net [15]) took a dense prediction approach and used encoder-decoder like CNN. As deduced from the results in Table 1, our proposed method based on SpaNet outperforms other state-of-the-art methods.…”

Section: Resultsmentioning

confidence: 99%

Nuclear Instance Segmentation Using a Proposal-Free Spatially Aware Deep Learning Framework

Koohbanani

Jahanifar²,

Gooya

et al. 2019

Lecture Notes in Computer Science

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Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to separate each nucleus. In this paper, we introduce a proposal-free deep learning based framework to address these challenges. To this end, we propose a spatially-aware network (SpaNet) to capture spatial information in a multi-scale manner. A dual-head variation of the SpaNet is first utilized to predict the pixel-wise segmentation and centroid detection maps of nuclei. Based on these outputs, a single-head SpaNet predicts the positional information related to each nucleus instance. Spectral clustering method is applied on the output of the last SpaNet, which utilizes the nuclear mask and the Gaussian-like detection map for determining the connected components and associated cluster identifiers, respectively. The output of the clustering method is the final nuclear instance segmentation mask. We applied our method on a publicly available multi-organ data set 5 and achieved state-of-the-art performance for nuclear segmentation.

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BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images

Cited by 71 publications

References 5 publications

CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

Nuclear Instance Segmentation Using a Proposal-Free Spatially Aware Deep Learning Framework

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