| 31. |
- Moshavegh, Ramin, et al.
(författare)
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Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis
- 2012
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781424441198 ; , s. 5372-5375
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Konferensbidrag (refereegranskat)abstract
- This paper presents an automated algorithm for robustly detecting and segmenting free-lying cell nuclei in bright-field microscope images of Pap smears. This is an essential initial step in the development of an automated screening system for cervical cancer based on malignancy associated change (MAC) analysis. The proposed segmentation algorithm makes use of gray-scale annular closings to identify free-lying nuclei-like objects together with marker-based watershed segmentation to accurately delineate the nuclear boundaries. The algorithm also employs artifact rejection based on size, shape, and granularity to ensure only the nuclei of intermediate squamous epithelial cells are retained. An evaluation of the performance of the algorithm relative to expert manual segmentation of 33 fields-of-view from 11 Pap smear slides is also presented. The results show that the sensitivity and specificity of nucleus detection is 94.71% and 85.30% respectively, and that the accuracy of segmentation, measured using the Dice coefficient, of the detected nuclei is 97.30±1.3%.
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| 32. |
- Norlén, Alexander, 1988, et al.
(författare)
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Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography
- 2016
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Ingår i: Journal of Mecial Imaging. - 2329-4302 .- 2329-4310. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- Recent findings indicate a strong correlation between the risk of future heart disease and the volume ofadipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delin-eation of the pericardium is extremely time-consuming and that existing methods for automatic delineation strugglewith accuracy. In this paper, an efficient and fully automatic approach to pericardium segmentation and epicardial fatvolume estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a randomforest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated Com-puter Tomography Angiography (CTA) volumes shows a significant improvement on state-of-the-art in terms of EFVestimation (mean absolute epicardial fat volume difference: 3.8 ml (4.7%), Pearson correlation: 0.99) with run-timessuitable for large-scale studies (52 s). Further, the results compare favorably to inter-observer variability measured on10 volumes.
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| 33. |
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| 34. |
- Persson, Mikael, 1959, et al.
(författare)
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Microwave based diagnostics and treatment in practice
- 2013
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Ingår i: 2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications, IMWS-BIO 2013 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- Globally, around 15 million people each year suffer a stroke. Only a small fraction of stroke patients who could benefit from thrombolytic treatment reach diagnosis and treatment in time. To increase this low figure we have developed microwave technology aiming to differentiate hemorrhagic from ischemic stroke patients. The standard method for breast cancer diagnosis today is X-ray mammography. Despite its recognized ability to detect tumors it suffers from some limitations. Neither the false positive nor the false negative detection rates are negligible. An interesting alternative being researched extensively today is microwave tomography. In our current strive to develop a clinical prototype we have found that the most suitable design consists of an antenna array placed in a full 3D pattern. During the last decade clinical studies have demonstrated the ability of microwave hyperthermia to dramatically enhance cancer patient survival. The fundamental challenge is to adequately heat deep-seated tumors while preventing surrounding healthy tissue from undesired heating and damage. We are specifically addressing the challenge to deliver power levels with spatial control, patient treatment planning, and noninvasive temperature measurements. © 2013 IEEE.
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| 35. |
- Qaiser, Mahmood, 1981
(författare)
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Automated Patient-Specific Multi-tissue Segmentation of MR Images of the Head
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The automated segmentation of magnetic resonance (MR) images of the human head is an active area of research in the field of neuroimaging. The resulting segmentation yields a patient-specific labeling of individual tissues and makes possible quantitative characterization of these tissues (e.g. in the study of Alzheimers disease and multiple sclerosis). The segmentation is also useful for assigning individual tissues conductivity or biomechanical properties for patient-specific electromagnetic and biomechanical simulations respectively. The former are of importance in applications such as EEG (electroencephalography) source localization in epilepsy patients and hyperthermia treatment planning for head and neck tumors. The latter are of interest in applications such as patient-specific motion correction and in surgical simulation.Automated and accurate segmentation of MR images is a challenging task in the field of neuroimaging because of noise, spatial intensity inhomogeneities, difficulty of MR intensity normalization and partial volume effects (a single voxel represents more than one tissue type). Consequently most of the techniques proposed to date require manual correction or intervention to achieve an accurate segmentation of the brain or whole-head. As a result they are time consuming,laborious and subjective. This thesis presents two automatic and unsupervised segmentation methods, for multi-tissue segmentation of the brain and whole-head respectively from multi-modal MR images, that are more accurate than the state-of-the-art algorithms. The brain segmentation method is based on the mean shift algorithm with a Bayesian-based adaptive bandwidth estimator. The method is called BAMS (Bayesian adaptive mean shift) and can be used to segment the brain into multiple tissue types; e.g. white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The accuracy of BAMS was evaluated relative to that of several competing methods using both synthetic and real MRI data. The results show that it is robust to both noise and spatial intensity in- homogeneities compared to competing methods. The whole-head segmentation method is based on a hierarchical segmentation approach (HSA) incorporating the BAMS method. The segmentation performance of HSA-BAMS was evaluated relative to a reference method BET-FAST (based on the BET and FAST tools in the well-known FMRIB Software Library) and three other instantiations of the HSA, using synthetic MRI data with varying noise levels, and real MRI data. The segmentation results show the efficacy and accuracy of proposed method and that it consistently outperforms the BET-FAST reference method. HSA-BAMS was also evaluated indirectly in terms of its impact on the accuracy of EEG source localization using electromagnetic simulations based on a tissue conductivity labeling derived from the segmentation. The results demonstrate that HSA-BAMS outperforms the competing methods, and suggest that it has potential as a surrogate for manual segmentation for EEG source localization.
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| 36. |
- Shakya, Snehlata, et al.
(författare)
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Multi-fiber estimation and tractography for diffusion mri using mixture of non-central wishart distributions
- 2017
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Ingår i: 2017 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2017. - : Eurographics Association. - 9783038680369 ; , s. 119-123
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Konferensbidrag (refereegranskat)abstract
- Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.
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| 37. |
- Spyretos, Christoforos, 1996-, et al.
(författare)
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Classification of Brain Tumour Tissue in Histopathology Images Using Deep Learning
- 2023
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Deep learning models have achieved prominent performance in digital pathology, with the potential to provide healthcare professionals with accurate decision-making assistance in their workflow. In this study, ViT and CNN models were implemented and compared for patch-level classification of four major glioblastoma tissue structures in histology images.A subset of the IvyGAP dataset (41 subjects, 123 images) was used, stain-normalised and patches of size 256x256 pixels were extracted. A per-subject split approach was applied to obtain training, validation and testing sets. Three models were implemented, a ViT and a CNN trained from scratch, and a ViT pre-trained on a different brain tumour histology dataset. The models' performance was assessed using a range of metrics, including accuracy and Matthew's correlation coefficient (MCC). In addition, calibration experiments were conducted and evaluated to align the models with the ground truth, utilising the temperature scaling technique. The models' uncertainty was estimated using the Monte Carlo dropout method. Lastly, the models were compared using the Wilcoxon signed-rank statistical significance test with Bonferroni correction.Among the models, the scratch-trained ViT obtained the highest test accuracy of 67% and an MCC of 0.45. The scratch-trained CNN reached a test accuracy of 49% and an MCC of 0.15, and the pre-trained ViT only achieved a test accuracy of 28% and an MCC of 0.034. Comparing the reliability graphs and metrics before and after applying temperature scaling, the subsequent experiments proceeded with the uncalibrated ViTs and the calibrated CNN. The calibrated CNN demonstrated moderate to high uncertainty across classes, and the ViTs had an overall high uncertainty. Statistically, there was no difference among the models at a significance level of 0.017. In conclusion, the scratch-trained ViT model considerably outperformed the scratch-trained CNN and the pre-trained ViT in classification. However, there was no statistically significant difference among the models.
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| 38. |
- Tampu, Iulian Emil, et al.
(författare)
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Diseased thyroid tissue classification in OCT images using deep learning: towards surgical decision support
- 2023
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Ingår i: Journal of Biophotonics. - : Wiley. - 1864-063X .- 1864-0648. ; 16:2
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Tidskriftsartikel (refereegranskat)abstract
- Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue. However, OCT images are difficult to interpret, thus, real-time automatic analysis could support the clinical decision-making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open-access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthews correlation coefficient (MCC) of 0.79 (accuracy = 0.90) for the normal-versus-abnormal classification. On the open-access datasets, the custom models achieved the best performance (MCC > 0.88, accuracy > 0.96). Results obtained for the normal-versus-abnormal classification suggest OCT, complemented with deep learning-based analysis, as a tool for real-time automatic diseased tissue identification in thyroid surgery.
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| 39. |
- Wang, Chunliang, 1980-, et al.
(författare)
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CT scan range estimation using multiple body parts detection : let PACS learn the CT image content
- 2016
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer. - 1861-6410 .- 1861-6429. ; 11:2, s. 317-325
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The aim of this study was to develop an efficient CT scan range estimation method that is based on the analysis of image data itself instead of metadata analysis. This makes it possible to quantitatively compare the scan range of two studies. Methods: In our study, 3D stacks are first projected to 2D coronal images via a ray casting-like process. Trained 2D body part classifiers are then used to recognize different body parts in the projected image. The detected candidate regions go into a structure grouping process to eliminate false-positive detections. Finally, the scale and position of the patient relative to the projected figure are estimated based on the detected body parts via a structural voting. The start and end lines of the CT scan are projected to a standard human figure. The position readout is normalized so that the bottom of the feet represents 0.0, and the top of the head is 1.0. Results: Classifiers for 18 body parts were trained using 184 CT scans. The final application was tested on 136 randomly selected heterogeneous CT scans. Ground truth was generated by asking two human observers to mark the start and end positions of each scan on the standard human figure. When compared with the human observers, the mean absolute error of the proposed method is 1.2 % (max: 3.5 %) and 1.6 % (max: 5.4 %) for the start and end positions, respectively. Conclusion: We proposed a scan range estimation method using multiple body parts detection and relative structure position analysis. In our preliminary tests, the proposed method delivered promising results.
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| 40. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Fast level-set based image segmentation using coherent propagation
- 2014
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Ingår i: Medical physics (Lancaster). - : John Wiley and Sons Ltd. - 0094-2405. ; 41:7
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images.
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