Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods

Basavanhally, Ajay; Yu, Elaine; Xu, Jun; Ganesan, Shridar; Feldman, Michael D.; Tomaszewski, John E.; Madabhushi, Anant

doi:10.1117/12.878092

Cited by 46 publications

(37 citation statements)

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“…Based on the processing primitives, we can categorize previous gland segmentation approaches into two types [9]: pixelbased method [1]- [3] and object-based method [5], [10], [11]. For the pixel-based method, the algorithm tries to assign each Fig.…”

Section: Related Workmentioning

confidence: 99%

“…We also compute a score to measure the quality of the contour. This score is defined as the maximum overlap between the detected contour and any ground-truth glands (11) We train a regressor to regress this score using the PHOG features. Recent research have shown that regression is sometimes more suitable than classification for object recognition [22], [23].…”

Section: Glandvision: Integrating Random Field Withmentioning

confidence: 99%

“…13, where all the proposed gland contours are sorted in the testing set according to the random field energy as defined in (3). The number beneath each image is the overlap score as defined in (11). It shows that four out of five images do not correspond to any glands.…”

Section: A Evaluating the Random Field Modelmentioning

confidence: 99%

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A Novel Polar Space Random Field Model for the Detection of Glandular Structures

Qiu

Shu

et al. 2014

IEEE Trans. Med. Imaging

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Abstract-In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundary of a gland. Next, we develop a visual feature-based support vector regressor to verify if the detected contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. Our approach can not only detect the existence of the gland, but also can accurately locate it with pixel accuracy. In the experiments, we treat the task of detecting glandular structures as object (gland) detection and segmentation problems respectively. The results indicate that our new technique outperforms state-of-the-art computer vision algorithms in respective fields.

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Section: Related Workmentioning

confidence: 99%

Section: Glandvision: Integrating Random Field Withmentioning

confidence: 99%

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A Novel Polar Space Random Field Model for the Detection of Glandular Structures

Qiu

Shu

et al. 2014

IEEE Trans. Med. Imaging

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“…Moreover, Nguyen and Srinivas 86 detected tumor nuclei and true lumen via a random forest classifier and classified tubules via graphcuts algorithm. In addition, the O'Callaghan neighborhoods adopted by Basavanhally et al 51 can distinguish tubules from lumen-like structures with low errors.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…By training sample images with pathologists' annotation, image analysis algorithms learn to identify and detect ROIs automatically. Numerous algorithms have been proposed to detect and segment cells, 47,48 mitosis, 49,50 and tissue architecture 51,52 in histopathology images.…”

Section: Image Preprocessingmentioning

confidence: 99%

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Chen

et al. 2017

Tumour Biol.

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With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

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Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading

Mathew

Ajith

Kini

et al. 2022

Int J Imaging Syst Tech

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Cancer grade is an indicator of the aggressiveness of cancer. It is used for prognosis and treatment decisions. Conventionally cancer grading is performed manually by experienced pathologists via microscopic examination of pathology slides. Among the three factors involved in breast cancer grading (mitosis count, nuclear atypia, and tubule formation), mitotic cell counting is the most challenging task for pathologists. It is possible to automate this task by applying computational algorithms on pathology slides images. Lack of sufficiently large datasets and class imbalance between mitotic and non-mitotic cells in slide images are the two major challenges in developing effective deep learning-based methods for mitosis detection. In this paper, we propose a new approach and a method based on that to address these challenges. The high training data requirement of the advanced deep neural network is met by combining two datasets from different sources after a color-normalization process. Class imbalance is addressed by the augmentation of the mitotic samples in a

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Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods

Cited by 46 publications

References 0 publications

A Novel Polar Space Random Field Model for the Detection of Glandular Structures

A Novel Polar Space Random Field Model for the Detection of Glandular Structures

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading

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