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- Gupta, Anindya, et al.
(författare)
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Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
- 2017
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Ingår i: Image Analysis. - Cham : Springer. - 9783319591254 ; , s. 407-418
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Konferensbidrag (refereegranskat)abstract
- Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.
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- Gupta, Anindya, et al.
(författare)
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Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks
- 2020
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Ingår i: International journal of imaging systems and technology (Print). - : Wiley. - 0899-9457 .- 1098-1098. ; 30:2, s. 327-339
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Tidskriftsartikel (refereegranskat)abstract
- Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error‐prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer‐assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate‐screening module and a false positive (FP) reduction module. The candidate‐screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features‐based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro‐nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well‐suited FP reduction module of a CAD scheme.
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- Gupta, Anindya, et al.
(författare)
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Weakly-supervised prediction of cell migration modes in confocal microscopy images using bayesian deep learning
- 2020
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Ingår i: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). - 9781538693308 - 9781538693315 ; , s. 1626-1629
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Konferensbidrag (refereegranskat)abstract
- Cell migration is pivotal for their development, physiology and disease treatment. A single cell on a 2D surface can utilize continuous or discontinuous migration modes. To comprehend the cell migration, an adequate quantification for single cell-based analysis is crucial. An automatized approach could alleviate tedious manual analysis, facilitating large-scale drug screening. Supervised deep learning has shown promising outcomes in computerized microscopy image analysis. However, their implication is limited due to the scarcity of carefully annotated data and uncertain deterministic outputs. We compare three deep learning models to study the problem of learning discriminative morphological representations using weakly annotated data for predicting the cell migration modes. We also estimate Bayesian uncertainty to describe the confidence of the probabilistic predictions. Amongst three compared models, DenseNet yielded the best results with a sensitivity of 87.91%±13.22 at a false negative rate of 1.26%±4.18.
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- Lidayová, Kristína, et al.
(författare)
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Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
- 2017
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Ingår i: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017. - Cham : Springer. - 9783319609638 ; , s. 182-194
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Konferensbidrag (refereegranskat)abstract
- Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.
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- Suveer, Amit, et al.
(författare)
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Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning
- 2019
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Ingår i: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). - : IEEE. - 9781538636411 ; , s. 548-551
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Konferensbidrag (refereegranskat)abstract
- Deep learning techniques have shown promising outcomes in single image super-resolution (SR) reconstruction from noisy and blurry low resolution data. The SR reconstruction can cater the fundamental. limitations of transmission electron microscopy (TEM) imaging to potentially attain a balance among the trade-offs like imaging-speed, spatial/temporal resolution, and dose/exposure-time, which is often difficult to achieve simultaneously otherwise. In this work, we present a convolutional neural network (CNN) model, utilizing both local and global skip connections, aiming for 4 x SR reconstruction of TEM images. We used exact image pairs of a calibration grid to generate our training and independent testing datasets. The results are compared and discussed using models trained on synthetic (downsampled) and real data from the calibration grid. We also compare the variants of the proposed network with well-known classical interpolations techniques. Finally, we investigate the domain adaptation capacity of the CNN-based model by testing it on TEM images of a cilia sample, having different image characteristics as compared to the calibration-grid.
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