Medicine and Health Sciences Commons^™

A Multi-Institutional Meningioma Mri Dataset For Automated Multi-Sequence Image Segmentation, Dominic Labella, Omaditya Khanna, Shan Mcburney-Lin, Ryan Mclean, Pierre Nedelec, Arif Rashid, Nourel Hoda Tahon, Talissa Altes, Ujjwal Baid, Radhika Bhalerao, Yaseen Dhemesh, Scott Floyd, Devon Godfrey, Fathi Hilal, Anastasia Janas, Anahita Kazerooni, Collin Kent, John Kirkpatrick, Florian Kofler, Kevin Leu, Nazanin Maleki, Bjoern Menze, Maxence Pajot, Zachary Reitman, Jeffrey Rudie, Rachit Saluja, Yury Velichko, Chunhao Wang, Pranav Warman, Nico Sollmann, David Diffley, Khanak Nandolia, Daniel Warren, Ali Hussain, John Pascal Fehringer, Yulia Bronstein, Lisa Deptula, Evan Stein, Mahsa Taherzadeh, Eduardo Portela De Oliveira, Aoife Haughey, Marinos Kontzialis, Luca Saba, Benjamin Turner, Melanie Brüßeler, Shehbaz Ansari, Athanasios Gkampenis, David Maximilian Weiss, Aya Mansour, Islam Shawali, Nikolay Yordanov, Joel Stein, Roula Hourani, Mohammed Yahya Moshebah, Ahmed Magdy Abouelatta, Tanvir Rizvi, Klara Willms, Dann Martin, Abdullah Okar, Gennaro D'Anna, Ahmed Taha, Yasaman Sharifi, Shahriar Faghani, Dominic Kite, Marco Pinho, Muhammad Ammar Haider, Michelle Alonso-Basanta, Javier Villanueva-Meyer, Andreas Rauschecker, Ayman Nada, Mariam Aboian, Adam Flanders, Spyridon Bakas, Evan Calabrese

Department of Radiology Faculty Papers

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by …

Prediction Of Rapid Early Progression And Survival Risk With Pre-Radiation Mri In Who Grade 4 Glioma Patients, Walia Farzana, Mustafa M. Basree, Norou Diawara, Zeina Shboul, Sagel Dubey, Marie M. Lockheart, Mohamed Hamza, Joshua D. Palmer, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Rapid early progression (REP) has been defined as increased nodular enhancement at the border of the resection cavity, the appearance of new lesions outside the resection cavity, or increased enhancement of the residual disease after surgery and before radiation. Patients with REP have worse survival compared to patients without REP (non-REP). Therefore, a reliable method for differentiating REP from non-REP is hypothesized to assist in personlized treatment planning. A potential approach is to use the radiomics and fractal texture features extracted from brain tumors to characterize morphological and physiological properties. We propose a random sampling-based ensemble classification model. The proposed …

Accurate Cytogenetic Biodosimetry Through Automated Dicentric Chromosome Curation And Metaphase Cell Selection, Jin Liu, Yanxin Li, Ruth Wilkins, Canadian Nuclear Laboratories, Joan H. Knoll, Peter Rogan

Biochemistry Publications

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs …

Mammographic Density Assessed On Paired Raw And Processed Digital Images And On Paired Screen-Film And Digital Images Across Three Mammography Systems, Anya Burton, Graham Byrnes, Jennifer Stone, Rulla M. Tamimi, John Heine, Celine Vachon, Vahit Ozmen, Ana Pereira, Maria Luisa Garmendia, Christopher Scott, John H. Hipwell, Caroline Dickens, Joachim Schüz, Mustafa Erkin Aribal, Kimberly Bertrand, Ava Kwong, Graham G. Giles, John Hopper, Beatriz Pérez Gómez, Marina Pollán, Soo-Hwang Teo, Shivaani Mariapun, Nur Aishah Mohd Taib, Martín Lajous, Ruy Lopez-Riduara, Megan Rice, Isabelle Romieu, Anath Arzee Flugelman, Giske Ursin, Samera Qureshi, Huiyan Ma, Eunjung Lee, Reza Sirous, Mehri Sirous, Jong Won Lee, Jisun Kim, Dorria Salem, Rasha Kamal, Mikael Hartman, Hui Miao, Kee-Seng Chia, Chisato Nagata, Sudhir Vinayak, Rose Ndumia, Carla H. Van Gils, Johanna O. P. Wanders, Beata Peplonska, Agnieszka Bukowska, Steve Allen, Sarah Vinnicombe, Sue Moss, Anna M. Chiarelli, Linda Linton, Gertraud Maskarinec, Martin J. Yaffe, Norman F. Boyd, Isabel Dos-Santos-Silva, Valerie A. Mccormack

Imaging & Diagnostic Radiology, East Africa

Background: Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/ digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.

Methods: We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (n = 3441), with each image pair read …

Computer Aided Detection Of Oral Lesions On Ct Images, Shaikat Mahmood Galib

Masters Theses

"Oral lesions are important findings on computed tomography images. They are difficult to detect on CT images because of low contrast, arbitrary orientation of objects, complicated topology and lack of clear lines indicating lesions. In this thesis, a fully automatic method to detect oral lesions from dental CT images is proposed to identify (1) Closed boundary lesions and (2) Bone deformation lesions. Two algorithms were developed to recognize these two types of lesions, which cover most of the lesion types that can be found on CT images. The results were validated using a dataset of 52 patients. Using non training …

Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo

Masters Theses

"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development …

Impact Of Varied Low Resolution Phantoms On Intensity Modulated Proton Therapy Dose Distributions, Aarohi Shyam Padhye

Theses Digitization Project

The primary purpose of this thesis is to discuss the usefulness of image segmentation techniques in creating accurate proton dose distribution plans. The calculation of the proton dose distribution has to take into account the material (tissue, bone, brain) in the treatment area of the patients body.

Imaging Breast Adipose And Fibroglandular Tissue Molecular Signatures By Using Hybrid Mri-Guided Near-Infrared Spectral Tomography, Ben Brooksby, Brian W. Pogue, Shudong Jiang, Hamid Dehghani, Subhadra Srinivasan, Christine Kogel, Tor D. Tosteson, John Weaver, Steven P. Poplack, Keith D. Paulsen

Dartmouth Scholarship

Magnetic resonance (MR)-guided near-infrared spectral tomography was developed and used to image adipose and fibroglandular breast tissue of 11 normal female subjects, recruited under an institutional review board-approved protocol. Images of hemoglobin, oxygen saturation, water fraction, and subcellular scattering were reconstructed and show that fibroglandular fractions of both blood and water are higher than in adipose tissue. Variation in adipose and fibroglandular tissue composition between individuals was not significantly different across the scattered and dense breast categories. Combined MR and near-infrared tomography provides fundamental molecular information about these tissue types with resolution governed by MR T1 images.

Near-Infrared Characterization Of Breast Tumors In Vivo Using Spectrally-Constrained Reconstruction, Subhadra Srinivasan, Brian W. Pogue, Ben Brooksby, Shudong Jiang, Hamid Dehghani, Christine Kogel, Wendy A. Wells, Steven P. Poplack, Keith D. Paulsen

Dartmouth Scholarship

Multi-wavelength Near-Infrared (NIR) Tomography was utilized in this study to non-invasively quantify physiological parameters of breast tumors using direct spectral reconstruction. Frequency domain NIR measurements were incorporated with a new spectrally constrained direct chromophore and scattering image reconstruction algorithm, which was validated in simulations and experimental phantoms. Images of total hemoglobin, oxygen saturation, water, and scatter parameters were obtained with higher accuracy than previously reported. Using this spectral approach, in vivo NIR images are presented and interpreted through a series of case studies (n=6 subjects) having differing abnormalities. The corresponding mammograms and ultrasound images are also evaluated. Three of six …