Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map

Naylor, Peter; Laé, Marick; Reyal, Fabien; Walter, Thomas

doi:10.1109/tmi.2018.2865709

Cited by 447 publications

(302 citation statements)

References 41 publications

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“…The one-to-one correspondences were first matched between the ground-truth box and the one detected by maximizing the Jaccard index. Then, the AJI was calculated similar to the Jaccard index; however, the falsely detected components were added to the denominator [48]:…”

Section: B Segmentation Performancementioning

confidence: 99%

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Chung

Lee

Hong

et al. 2020

Computers in Biology and Medicine

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Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the interoverlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.Index Terms-Cone beam computed tomography image segmentation, pose-aware tooth detection, pose regression neural network, tooth instance segmentation.

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Section: B Segmentation Performancementioning

confidence: 99%

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Chung

Lee

Hong

et al. 2020

Computers in Biology and Medicine

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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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“…The proposed counting methods are applied to the counting of cells from histological slides [10]. This dataset has 33 labelled slides of dimension 512 × 512, taken from 7 different types of tissue, and each slide has an associated cell label map used here as the count ground truth.…”

Section: Datamentioning

confidence: 99%

As Easy as 1, 2...4? Uncertainty in Counting Tasks for Medical Imaging

Eaton-Rosen

Varsavsky

Ourselin

et al. 2019

Lecture Notes in Computer Science

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Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and suggest ways of deriving predictive intervals from these. We then propose and test a method for calculating intervals as an output of a multi-task network. These predictive intervals are optimised to be as narrow as possible, while also enclosing a desired percentage of the data. We demonstrate the effectiveness of this technique on histopathological cell counting and white matter hyperintensity counting. Finally, we offer insight into other areas where this technique may apply.

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Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map

Cited by 447 publications

References 41 publications

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

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

As Easy as 1, 2...4? Uncertainty in Counting Tasks for Medical Imaging

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