Multi-scale Cell Instance Segmentation with Keypoint Graph Based Bounding Boxes

Yi, Jingru; Wu, Pengxiang; Huang, Qi‐Fang; Qu, Hui; Liu, Bo; Hoeppner, Daniel J.; Metaxas, Dimitris N.

doi:10.1007/978-3-030-32239-7_41

Cited by 52 publications

(44 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Comparisons on the DSB2018 database: We compare the performance of CPP-Net with Mask-RCNN [2], [13], KeypointGraph [19], HoVer-Net [4], PatchPerPix [17], and StarDist [13]. The results of this comparison are tabulated in Table IV.…”

Section: E Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…As shown in Compared with other approaches presented in Table VI, CPP-Net and StarDist are more efficient owing to their light-weight backbone and their simple post-processing operations. [19] 0.8556 HoVer-Net [4] 1.5556 PatchPerPix [17] 5.8767 StarDist [13] 0.2327 CPP-Net 0.2519…”

Section: E Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…Moreover, it is worth noting that BBBC006 consists of two types of images, specifically Hoechst images and phalloidin images. The latter image type contains a significant amount of noise, which affects the performance of KeypointGraph [19] and InstanceEmbedding [20]. In comparison, StarDist, CPP-Net, and HoVer-Net continues to achieve promising results, which shows their robustness when processing noisy images.…”

Section: E Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

Section: E Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

Section: E Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

Section: E Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Chen

Ding

Tao

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Mahmood et al [20] used a dual GAN that learns to transform masks, including polygons, to synthetic histo-pathological patches. Bailo et al [34] [35], KG instance segmentation [36]). The conventional methods investigated were specifically designed to solve image segmentation problems on fluorescence nuclear images and consist of a marker-based approach (Iterative hmin) and a model-based approach (ARG).…”

Section: Data Augmentation and Artificial Image Synthesismentioning

confidence: 99%

“…5) KG instance segmentation: The keypoint graph instance segmentation (KG instance segmentation) network [36] was developed to tackle cell instance segmentation tasks. In contrast to approaches such as the aforementioned Mask R-CNN, which typically utilize anchor box based detectors, keypointbased detectors in combination with multi-scale feature maps are used.…”

Section: Data Augmentation and Artificial Image Synthesismentioning

confidence: 99%

Evaluation of Deep Learning Architectures for Complex Immunofluorescence Nuclear Image Segmentation

Kromp

Fischer

Bozsaky

et al. 2021

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Separating and labeling each nuclear instance (instance-aware segmentation) is the key challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been demonstrated to solve nuclear image segmentation tasks across different imaging modalities, but a systematic comparison on complex immunofluorescence images has not been performed. Deep learning based segmentation requires annotated datasets for training, but annotated fluorescence nuclear image datasets are rare and of limited size and complexity. In this work, we evaluate and compare the segmentation effectiveness of multiple deep learning architectures (U-Net, U-Net ResNet, Cellpose, Mask R-CNN, KG instance segmentation) and two conventional algorithms (Iterative h-min based watershed, Attributed relational graphs) on complex fluorescence nuclear images of various types.We propose and evaluate a novel strategy to create artificial images to extend the training set. Results show that instanceaware segmentation architectures and Cellpose outperform the U-Net architectures and conventional methods on complex images in terms of F1 scores, while the U-Net architectures achieve overall higher mean Dice scores. Training with additional artificially generated images improves recall and F1 scores for complex images, thereby leading to top F1 scores for three out of five sample preparation types. Mask R-CNN trained on artificial images achieves the overall highest F1 score on complex images of similar conditions to the training set images while Cellpose achieves the overall highest F1 score on complex images of new imaging conditions. We provide quantitative results demonstrat-Manuscript resubmitted on February 13, 2021.

show abstract

W‐Transformer: Accurate Cobb angles estimation by using a transformer‐based hybrid structure

Yao

Gao

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Background: Scoliosis is a type of spinal deformity, which is harmful to a person's health. In severe cases, it can trigger paralysis or death. The measurement of Cobb angle plays an essential role in assessing the severity of scoliosis. Purpose: The aim of this paper is to propose an automatic system for landmark detection and Cobb angle estimation, which can effectively help clinicians diagnose and treat scoliosis. Methods: A novel hybrid framework was proposed to measure Cobb angle precisely for clinical diagnosis, which was referred as W-Transformer due to its wshaped architecture. First, a convolutional neural network of cascade residual blocks as our backbone was designed. Then a transformer was fused to learn the dependency information between spine and landmarks. In addition, a reinforcement branch was designed to improve the overlap of landmarks, and an improved prediction module was proposed to fine-tune the final coordinates of landmarks in Cobb angles estimation. Besides, the public Accurate Automated Spinal Curvature Estimation (AASCE) MICCAI 2019 challenge was served as data set. It supplies 609 manually labeled spine anterior-posterior (AP) X-ray images, each of which contains a total of 68 landmark labels and three Cobb Angles tags. Results: From the perspective of the AASCE MICCAI 2019 challenge, we achieved a lower symmetric mean absolute percentage error (SMAPE) of 8.26% for all Cobb angles and the lowest averaged detection error of 50.89 in terms of landmark detection, compared with many state-of -the-art methods. We also provided the SMAPEs for the Cobb angles of the proximal-thoracic (PT), the main-thoracic (MT), and the thoracic-lumbar (TL) area, which are 5.27%, 14.59%, and 20.97% respectively, however, these data were not covered in most previous studies. Statistical analysis demonstrates that our model has obtained a high level of Pearson correlation coefficient of 0.9398 (p < 0.001), which shows excellent reliability of our model. Our model can yield 0.9489 (p < 0.001), 0.8817 (p < 0.001), and 0.9149 (p < 0.001) for PT, MT, and TL, respectively. The overall variability of Cobb angle measurement is less than 4 • , implying clinical value. And the mean absolute deviation (standard deviation) for three regions is 3.64 • (4.13 • ), 3.84 • (4.66 • ), and 3.80 • (4.19 • ). The results of Student paired t-test indicate that no statistically significant differences are observed between manual measurement and our automatic approach (p-value is always >0.05). Regarding the diagnosis of scoliosis (Cobb angle >10 • ), the proposed method achieves a high sensitivity of 0.9577 and a specificity of 0.8475 for all spinal regions. Conclusions: This study offers a brand-new automatic approach that is potentially of great benefit of the complex task of landmark detection and Cobb angle 3246

show abstract

Multi-scale Cell Instance Segmentation with Keypoint Graph Based Bounding Boxes

Cited by 52 publications

References 14 publications

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Evaluation of Deep Learning Architectures for Complex Immunofluorescence Nuclear Image Segmentation

W‐Transformer: Accurate Cobb angles estimation by using a transformer‐based hybrid structure

Contact Info

Product

Resources

About