Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Lee, Soonam; Fu, Chichen; Schulze‐Lefert, Paul; Dunn, Kenneth W.; Delp, Edward J.

doi:10.2352/issn.2470-1173.2018.15.coimg-199

Cited by 10 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Microscopy Data-II To analyze the detection accuracy of the locations of the nuclei found with the counting methods, we use the object-based evaluation as described in [28]. The evaluation is based on manually annotated groundtruth of two subvolumes from I Data−I with size of 128 × 128 × 64 and from I Data−II with size of 512 × 512 × 16.…”

Section: Resultsmentioning

confidence: 99%

Nuclei counting in microscopy images with three dimensional generative adversarial networks

Han

Lee

et al. 2019

Medical Imaging 2019: Image Processing

Self Cite

View full text Add to dashboard Cite

Microscopy image analysis can provide substantial information for clinical study and understanding of biological structures. Two-photon microscopy is a type of fluorescence microscopy that can image deep into tissue with nearinfrared excitation light. We are interested in methods that can detect and characterize nuclei in 3D fluorescence microscopy image volumes. In general, several challenges exist for counting nuclei in 3D image volumes. These include crowding and touching of nuclei, overlapping of nuclei, and shape and size variances of the nuclei. In this paper, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN and used to train another 3D GAN that counts the number of nuclei. Our approach is evaluated with respect to the number of groundtruth nuclei and compared with common ways of counting used in the biological research. Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The accuracy results of proposed nuclei counter are compared with the ImageJ's 3D object counter (JACoP) and the 3D watershed. Both the counting accuracy and the object-based evaluation show that the proposed technique is successful for counting nuclei in 3D.

show abstract

Section: Resultsmentioning

confidence: 99%

Nuclei counting in microscopy images with three dimensional generative adversarial networks

Han

Lee

et al. 2019

Medical Imaging 2019: Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Nottingham Histological Grading (NHG) system is currently the most commonly utilized tool for assessing the aggressiveness of breast cancer ( 50 ). According to this system, breast cancer scores are determined based on three significant factors: tubule formation ( 51 ), nuclear pleomorphism ( 52 ), and mitotic count ( 53 ). Tubule formation is an essential assessment factor in the NHG grading system for understanding the level of cancer.…”

Section: Breast Cancer Prediction Using Deep Learningmentioning

confidence: 99%

Mammography with deep learning for breast cancer detection

Wang

2024

Front. Oncol.

View full text Add to dashboard Cite

X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

show abstract

“…Some of the successful proposed techniques for healthcare purposes are detecting diabetic retinopathy [41] and detecting melanoma [42]. Several studies have applied the above techniques in order to detect prostate carcinoma biopsies [43] and to segment epithelium [44], tubules [45], lymphomas [46] and mitosis [47]. The effectiveness of CNNs in detecting invasive ductal carcinoma [48] has been demonstrated by their higher scores and balanced accuracy.…”

Section: Computational Pathologymentioning

confidence: 99%

Domain Generalization Across Multi-centre Digital Pathology Images for Lymph Node Segmentation and Classification

Dambandkhameneh

2024

Preprint

View full text Add to dashboard Cite

Within the last few years, medical deep learning has experienced rapid advances, especially in the area of image interpretation. Using whole-slide images of histological lymph node sections, this thesis develops deep learning models and techniques for detecting and classifying breast cancer metastases. Pathologists would normally have to perform extensive microscopic assessments on this clinically relevant task. In patients with breast cancer, metastases in lymph nodes have therapeutic implications. So, an automated solution may remarkably reduce pathologists’ workload while at the same time reducing the subjectivity of their diagnoses. There are, however, still several significant challenges associated with the development and translation of medical deep learning systems. To begin with, developing large and well-annotated datasets is expensive and most often, the labels are imbalanced. In addition, it is difficult to transfer the performance of deep learning algorithms from one dataset and setting to another due to domain shift issues. Lastly, the results of deep learning systems should be comprehensible and applicable to clinical datasets. In order to enhance effectiveness in an unknown target domain and increase generalization, this thesis assesses ensemble learning by transferring prior knowledge from non-medical and medical sources. Although deep learning methods may be successful, they may not perform well in a clinical workflow. Many datasets are constructed from millions of patches of images, leading to a data curation bias; others only contain annotations at the slide-level, making it difficult to detect errors at the local level as long as the results are correct. As a way of alleviating the class imbalance and biased training data, this thesis proposes a cluster-based sampling method for whole-slide histopathology image analysis. With the proposed ensemble learning and sampling methods, cutting-edge machine learning architectures can be extended, and state-of-the-art performance can be achieved for both diagnostic test images and whole slide images. Results in the sampling part show that using the same dataset, the results are roughly the same, regardless of sampling methods. However, the result of the unseen dataset falls dramatically when it is sampled randomly. By sampling separately, we reduce the probability of drops in the results for unseen datasets. Random selection gives a sensitivity of 0.82 on an unseen dataset, however sampling separately gives a sensitivity of 0.93. In random selection, dice index similarity is 0.83, whereas in sampling separately, dice index similarity is 0.90. Also, observing the slide level results in St. Michael’s hospital dataset, it can be concluded that we cannot decide about the best model and all the models give good results at least in one slide. So, combining the results from multiple configurations would give a more reliable and consistent result.

show abstract

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Cited by 10 publications

References 33 publications

Nuclei counting in microscopy images with three dimensional generative adversarial networks

Nuclei counting in microscopy images with three dimensional generative adversarial networks

Mammography with deep learning for breast cancer detection

Domain Generalization Across Multi-centre Digital Pathology Images for Lymph Node Segmentation and Classification

Contact Info

Product

Resources

About