Faster R-CNN approach for detection and quantification of DNA damage in comet assay images

Rosati, Riccardo; Romeo, Luca; Silvestri, Sonia; Marcheggiani, Fabio; Tiano, Luca; Frontoni, Emanuele

doi:10.1016/j.compbiomed.2020.103912

Cited by 33 publications

(22 citation statements)

References 37 publications

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“…In fact, the handcrafted-based approaches have been overcame by deep learning (DL) [21,22,23], which directly extracts features from raw data, avoiding their explicit mathematical formulation, but requires larger training datasets [24]. The work in [25] proposes an active-contour model guided by external forces that are derived with a CNN to segment the fetal head.…”

Section: Related Workmentioning

confidence: 99%

A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objectives Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intraand inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. Methods The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. Results The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 (± 1.76) mm and a Dice similarity coefficient of 97.75 (± 1.32) % were achieved, overcoming approaches in the literature. Conclusions The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.

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Section: Related Workmentioning

confidence: 99%

A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

Self Cite

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“…FRCNN merges RPN and Fast R-CNN into a unified network by sharing the convolutional features with “attention” mechanisms, which greatly improves both the time and accuracy of target detection [ 13 , 16 ]. Indeed, FRCNN has shown higher detection performance in the biomedical filed than other state-of-the-art methods, such as support vector machines (SVMs), visual geometry Group-16 (VGG-16), single shot multibox detectors (SSDs), and you only look once (YOLO), in terms of time and accuracy [ 19 , 20 , 21 ]. In particular, FRCNN has achieved the best performance for diabetic foot ulcer (DFU) detection; the purpose of the DFU study was similar to our research goal [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai¹,

Yamazaki²,

Hirano

et al. 2020

Biomolecules

156

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Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

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“…For instance, CellCognition aims at annotating complex cellular dynamics in live-cell microscopic movies [19] by combining Support Vector Machines with hidden Markov models to evaluate the progression by using morphologically distinct biological states. More recently, it has been shown that methods based on Deep Convolutional Neural Networks (DCNNs) [20,21] can successfully address detection and segmentation problems otherwise difficult to solve by exploiting traditional image processing methods [22]. Further, an ensemble of DCNNs defined to segment cell images was presented in [23], where a gating network automatically divides the input image into several sub-problems and assigns them to specialized networks, allowing for a more efficient learning with respect to a single DCNN.…”

Section: Introductionmentioning

confidence: 99%

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

et al. 2020

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Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

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Faster R-CNN approach for detection and quantification of DNA damage in comet assay images

Cited by 33 publications

References 37 publications

A regression framework to head-circumference delineation from US fetal images

A regression framework to head-circumference delineation from US fetal images

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

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