Collaborating Authors

Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN Machine Learning

Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1- score using a challenging dataset that contains the data under four different conditions.

Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks

AAAI Conferences

The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma.However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice.In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity.Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics.Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed.

A new mitotic activity comes into focus


During mitosis, each duplicated chromosome must be accurately attached to the microtubule spindle, which pulls the chromosomes to opposite poles of the cell, where they are segregated to daughter cells. A number of mitotic kinases orchestrate mitosis to ensure accurate segregation, including cyclin-dependent kinase 1 (CDK1), the Polo-like kinases, and the Aurora kinases (1). The kinase ATR (ataxia telangiectasia and Rad3-related), which is involved in DNA damage responses during interphase of the cell cycle, has also been shown to prevent chromosome segregation errors (2). However, this role of ATR was presumed to be an indirect effect. On page 108 of this issue, Kabeche et al. (3) unveil a mitosis-specific ATR activity that ensures proper chromosome segregation and that this activity is dependent on a specific three-stranded nucleic acid structure known as an R loop.

[Report] A force-generating machinery maintains the spindle at the cell center during mitosis


As a cell divides, the mitotic spindle segregates chromosomes to daughter cells. It does so while maintaining a centralized position; however, the mechanism for spindle placement is unclear. Garzon-Coral et al. used magnetic tweezers to show that the dynamical properties of the astral microtubules act as a force-generating machinery to keep the spindle at the cell center. This machinery is strong enough to quench thermal fluctuations to ensure precise localization of the spindle, but soft enough to allow molecular force generators to fine-tune the position of the mitotic spindle.

Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images Machine Learning

Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression. However, automated mitotic nuclei detection poses several challenges because of the unavailability of pixel-level annotations, different morphological configurations of mitotic nuclei, their sparse representation, and close resemblance with non-mitotic nuclei. These challenges undermine the precision of the automated detection model and thus make detection difficult in a single phase. This work proposes an end-to-end detection system for mitotic nuclei identification in breast cancer histopathological images. Deep object detection-based Mask R-CNN is adapted for mitotic nuclei detection that initially selects the candidate mitotic region with maximum recall. However, in the second phase, these candidate regions are refined by multi-object loss function to improve the precision. The performance of the proposed detection model shows improved discrimination ability (F-score of 0.86) for mitotic nuclei with significant precision (0.86) as compared to the two-stage detection models (F-score of 0.701) on TUPAC16 dataset. Promising results suggest that the deep object detection-based model has the potential to learn the characteristic features of mitotic nuclei from weakly annotated data and suggests that it can be adapted for the identification of other nuclear bodies in histopathological images.