Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography

Background: Diffusion imaging tractography is increasingly used to trace critical fiber tracts in brain tumor patients to reduce the risk of post-operative neurological deficit. However, the effects of peritumoral edema pose a challenge to conventional tractography using the standard diffusion tensor model. The aim of this study was to present a novel technique using a two-tensor unscented Kalman filter (UKF) algorithm to track the arcuate fasciculus (AF) in brain tumor patients with peritumoral edema. Methods: Ten right-handed patients with left-sided brain tumors in the vicinity of language-related cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-Tesla magnetic resonance imaging (MRI) including a diffusion-weighted dataset with 31 directions. Fiber tractography was performed using both single-tensor streamline and two-tensor UKF tractography. A two-regions-of-interest approach was applied to perform the delineation of the AF. Results from the two different tractography algorithms were compared visually and quantitatively. Results: Using single-tensor streamline tractography, the AF appeared disrupted in four patients and contained few fibers in the remaining six patients. Two-tensor UKF tractography delineated an AF that traversed edematous brain areas in all patients. The volume of the AF was significantly larger on two-tensor UKF than on single-tensor streamline tractography (p < 0.01). Conclusions: Two-tensor UKF tractography provides the ability to trace a larger volume AF than single-tensor streamline tractography in the setting of peritumoral edema in brain tumor patients

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.Comment: 10 pages, 4 figure

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation

Navigering av nål vid bildstyrd brachyterapi av gynekologisk cancer

In the past twenty years, the combination of the advances in medical imaging technologies and therapeutic methods had a great impact in developing minimally invasive interventional procedures. Although the use of medical imaging for the surgery and therapy guidance dates back to the early days of x-ray discovery, there is an increasing evidence in using the new imaging modalities such as computed tomography (CT), magnetic reso- nance imaging (MRI) and ultrasound in the operating rooms. The focus of this thesis is on developing image-guided interventional methods and techniques to support the radiation therapy treatment of gynecologic cancers. Gynecologic cancers which involves malignan- cies of the uterus, cervix, vagina and the ovaries are one of the top causes of mortality and morbidity among the women in U.S. and worldwide. The common treatment plan for radiation therapy of gynecologic cancers is chemotherapy and external beam radiation therapy followed by brachytherapy. Gynecological brachytherapy involves placement of interstitial catheters in and around the tumor area, often with the aid of an applicator. The goal is to create an optimal brachytherapy treatment plan that leads to maximal radiation dose to the cancerous tissue and minimal destructive radiation to the organs at risk. The accuracy of the catheter placement has a leading effect in the success of the treatment. However there are several techniques are developed for navigation of catheters and needles for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is obviously lacking for gynecologic brachytherapy procedures. This thesis proposes a technique which aims to increase the accuracy and efficiency of catheter placements in gynecologic brachytherapy by guiding the catheters with an electromagnetic tracking system. To increase the accuracy of needle placement a navigation system has been set up and the appropriate software tools were developed and released for the public use as a module in the open-source 3D Slicer software. The developed technology can be translated from benchmark to the bedside to offer the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs including bladder, rectum and bowel. To test the designed system two independent experiments were designed and performed on a phantom model in order to evaluate the targeting accuracy of the tracking system and the mean targeting error over all experiments was less than 2.9 mm, which can be compared to the targeting errors in the available commercial clinical navigation systems

Navigering av nål vid bildstyrd brachyterapi av gynekologisk cancer

In the past twenty years, the combination of the advances in medical imaging technologies and therapeutic methods had a great impact in developing minimally invasive interventional procedures. Although the use of medical imaging for the surgery and therapy guidance dates back to the early days of x-ray discovery, there is an increasing evidence in using the new imaging modalities such as computed tomography (CT), magnetic reso- nance imaging (MRI) and ultrasound in the operating rooms. The focus of this thesis is on developing image-guided interventional methods and techniques to support the radiation therapy treatment of gynecologic cancers. Gynecologic cancers which involves malignan- cies of the uterus, cervix, vagina and the ovaries are one of the top causes of mortality and morbidity among the women in U.S. and worldwide. The common treatment plan for radiation therapy of gynecologic cancers is chemotherapy and external beam radiation therapy followed by brachytherapy. Gynecological brachytherapy involves placement of interstitial catheters in and around the tumor area, often with the aid of an applicator. The goal is to create an optimal brachytherapy treatment plan that leads to maximal radiation dose to the cancerous tissue and minimal destructive radiation to the organs at risk. The accuracy of the catheter placement has a leading effect in the success of the treatment. However there are several techniques are developed for navigation of catheters and needles for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is obviously lacking for gynecologic brachytherapy procedures. This thesis proposes a technique which aims to increase the accuracy and efficiency of catheter placements in gynecologic brachytherapy by guiding the catheters with an electromagnetic tracking system. To increase the accuracy of needle placement a navigation system has been set up and the appropriate software tools were developed and released for the public use as a module in the open-source 3D Slicer software. The developed technology can be translated from benchmark to the bedside to offer the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs including bladder, rectum and bowel. To test the designed system two independent experiments were designed and performed on a phantom model in order to evaluate the targeting accuracy of the tracking system and the mean targeting error over all experiments was less than 2.9 mm, which can be compared to the targeting errors in the available commercial clinical navigation systems

Navigering av nål vid bildstyrd brachyterapi av gynekologisk cancer

In the past twenty years, the combination of the advances in medical imaging technologies and therapeutic methods had a great impact in developing minimally invasive interventional procedures. Although the use of medical imaging for the surgery and therapy guidance dates back to the early days of x-ray discovery, there is an increasing evidence in using the new imaging modalities such as computed tomography (CT), magnetic reso- nance imaging (MRI) and ultrasound in the operating rooms. The focus of this thesis is on developing image-guided interventional methods and techniques to support the radiation therapy treatment of gynecologic cancers. Gynecologic cancers which involves malignan- cies of the uterus, cervix, vagina and the ovaries are one of the top causes of mortality and morbidity among the women in U.S. and worldwide. The common treatment plan for radiation therapy of gynecologic cancers is chemotherapy and external beam radiation therapy followed by brachytherapy. Gynecological brachytherapy involves placement of interstitial catheters in and around the tumor area, often with the aid of an applicator. The goal is to create an optimal brachytherapy treatment plan that leads to maximal radiation dose to the cancerous tissue and minimal destructive radiation to the organs at risk. The accuracy of the catheter placement has a leading effect in the success of the treatment. However there are several techniques are developed for navigation of catheters and needles for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is obviously lacking for gynecologic brachytherapy procedures. This thesis proposes a technique which aims to increase the accuracy and efficiency of catheter placements in gynecologic brachytherapy by guiding the catheters with an electromagnetic tracking system. To increase the accuracy of needle placement a navigation system has been set up and the appropriate software tools were developed and released for the public use as a module in the open-source 3D Slicer software. The developed technology can be translated from benchmark to the bedside to offer the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs including bladder, rectum and bowel. To test the designed system two independent experiments were designed and performed on a phantom model in order to evaluate the targeting accuracy of the tracking system and the mean targeting error over all experiments was less than 2.9 mm, which can be compared to the targeting errors in the available commercial clinical navigation systems

Machine learning for MRI-guided prostate cancer diagnosis and interventions

Prostate cancer is the second most prevalent cancer in men worldwide. Magnetic Resonance Imaging (MRI) is widely used for prostate cancer diagnosis and guiding biopsy procedures due to its ability in providing superior contrast between cancer and adjacent soft tissue. Appropriate clinical management of prostate cancer critically depends on meticulous detection and characterization of the disease and precise biopsy procedures if necessary. The goal of this thesis is to develop computational methods to aid radiologists in diagnosing prostate cancer in MRI and planning necessary interventions. To this end, we have developed novel methods for assessing probability of clinically significant prostate cancer in MRI, localizing biopsy needles in MRI, and providing segmentation of structures such as the prostate gland. The proposed methods in this thesis are based on supervised machine learning techniques, in particular deep convolutional neural networks (CNNs). We have also developed methodology that is necessary in order for such deep networks to eventually be useful in clinical decision-making workflows; this spans the areas of domain adaptation, confidence calibration, and uncertainty estimation for CNNs. We used domain adaptation to transfer the knowledge of lesion segmentation learned from MRI images obtained using one set of acquisition parameters to another. We also studied predictive uncertainty in the context of medical image segmentation to provide model confidence (i.e expectation of success) at inference time. We further proposed parameter ensembling by perturbation for calibration of neural networks.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat