Mask-RCNN and U-net Ensembled for Nuclei Segmentation
Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the right model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.
Code (1)
Tasks
SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adapting Mask-RCNN for Automatic Nucleus Segmentation
Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorit…
Instance SegmentationObjectobject-detectionObject Detection+3Entropy Bootstrapping for Weakly Supervised Nuclei Detection
Microscopy structure segmentation, such as detecting cells or nuclei, generally requires a human to draw a ground truth contour around each instance. Weakly supervised approaches (e.g. consisting of only single point lab…
Instance SegmentationSegmentationSemantic SegmentationIdentify the cells' nuclei based on the deep learning neural network
Identify the cells' nuclei is the important point for most medical analyses. To assist doctors finding the accurate cell' nuclei location automatically is highly demanded in the clinical practice. Recently, fully convolu…
Image SegmentationSegmentationSemantic SegmentationWeak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images
We present a weak to strong generalization methodology for fully automated training of a multi-head extension of the Mask-RCNN method with efficient channel attention for reliable segmentation of overlapping cell nuclei …
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational patholog…
Instance SegmentationNuclear SegmentationSegmentationSemantic Segmentation