Browsing by Subject "Segmentation"
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Item Digital Image Analysis Tools Developed by the Indiana O’Brien Center(Frontiers Media, 2021-12-16) Dunn, Kenneth W.; Medicine, School of MedicineThe scale and complexity of images collected in biological microscopy have grown enormously over the past 30 years. The development and commercialization of multiphoton microscopy has promoted a renaissance of intravital microscopy, providing a window into cell biology in vivo. New methods of optical sectioning and tissue clearing now enable biologists to characterize entire organs at subcellular resolution. New methods of multiplexed imaging support simultaneous localization of forty or more probes at a time. Exploiting these exciting new techniques has increasingly required biomedical researchers to master procedures of image analysis that were once the specialized province of imaging experts. A primary goal of the Indiana O'Brien Center has been to develop robust and accessible image analysis tools for biomedical researchers. Here we describe biomedical image analysis software developed by the Indiana O'Brien Center over the past 25 years.Item Image Segmentation, Parametric Study, and Supervised Surrogate Modeling of Image-based Computational Fluid Dynamics(2022-05) Islam, Md Mahfuzul; Yu, Huidan (Whitney); Du, Xiaoping; Wagner, DianeWith the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work. To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. This study incorporates a new computational platform, called InVascular, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI. As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.Item Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study(Elsevier, 2024-12-26) Lee, Youngjun; Bandara, Wikum R.; Park, Sangjun; Lee, Miran; Seo, Choongboem; Yang, Sunwoo; Lim, Kenneth J.; Moe, Sharon M.; Warden, Stuart J.; Surowiec, Rachel K.; Medicine, School of MedicineHigh resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site. Manually annotated cortical bone was used to train each segmentation deep-learning model. Textural features were extracted via Gray-Level Co-occurrence Matrix (GLCM) and classified as CKD or non-CKD using XGBoost with each segmentation model. For comparison, manufacturer-supplied segmentation was used to extract cortical geometry, microarchitecture, and finite element analysis (FEA) outcomes. Model performance was confirmed using the test dataset and a separate independent validation cohort which included HRpQCT imaging from 42 additional individuals (18 non-CKD, 24 CKD stage 5D). For segmentation, the diaphyseal location showed strong performance on test datasets, with Mean IoUs of 0.96 and 0.95, and accuracies of 0.97 for both radius and tibia sites in CKD. Model 4 developed from the diaphyseal tibia region excelled in classifying test and independent validation datasets, achieving F1 scores of 0.99 and 0.96, AUCs of 0.99 and 0.94, sensitivities of 0.99, and specificities of 0.99 and 0.92. No single parameter, including BMD and cortical porosity, among conventional CT outcomes consistently differentiated CKD from non-CKD across all anatomical sites. Integrating HRpQCT with deep and machine learning, this innovative approach enables precise automatic segmentation of severely deteriorated endocortical surfaces and enhances sensitivity to CKD-related cortical bone changes compared to standard DXA and HRpQCT outcomes.Item Machine Vision Assisted In Situ Ichthyoplankton Imaging System(2013-07-12) Iyer, Neeraj; Tsechpenakis, Gavriil; Raje, Rajeev; Tuceryan, Mihran; Fang, ShiaofenRecently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.Item Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications(MDPI, 2022-02-25) Wu, Yawen; Cheng, Michael; Huang, Shuo; Pei, Zongxiang; Zuo, Yingli; Liu, Jianxin; Yang, Kai; Zhu, Qi; Zhang, Jie; Hong, Honghai; Zhang, Daoqiang; Huang, Kun; Cheng, Liang; Shao, Wei; Medicine, School of MedicineWith the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.Item The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI(ArXiv, 2023-06-01) Moawad, Ahmed W.; Janas, Anastasia; Baid, Ujjwal; Ramakrishnan, Divya; Jekel, Leon; Krantchev, Kiril; Moy, Harrison; Saluja, Rachit; Osenberg, Klara; Wilms, Klara; Kaur, Manpreet; Avesta, Arman; Cassinelli Pedersen, Gabriel; Maleki, Nazanin; Salimi, Mahdi; Merkaj, Sarah; von Reppert, Marc; Tillmans, Niklas; Lost, Jan; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Maresca, Ryan; Link, Katherine E.; Tahon, Nourel Hoda; Marcus, Daniel; Sotiras, Aristeidis; LaMontagne, Pamela; Chakrabarty, Strajit; Teytelboym, Oleg; Youssef, Ayda; Nada, Ayaman; Velichko, Yuri S.; Gennaro, Nicolo; Connectome Students; Group of Annotators; Cramer, Justin; Johnson, Derek R.; Kwan, Benjamin Y. M.; Petrovic, Boyan; Patro, Satya N.; Wu, Lei; So, Tiffany; Thompson, Gerry; Kam, Anthony; Guzman Perez-Carrillo, Gloria; Lall, Neil; Group of Approvers; Albrecht, Jake; Anazodo, Udunna; Lingaru, Marius George; Menze, Bjoern H.; Wiestler, Benedikt; Adewole, Maruf; Anwar, Syed Muhammad; Labella, Dominic; Li, Hongwei Bran; Iglesias, Juan Eugenio; Farahani, Keyvan; Eddy, James; Bergquist, Timothy; Chung, Verena; Shinohara, Russel Takeshi; Dako, Farouk; Wiggins, Walter; Reitman, Zachary; Wang, Chunhao; Liu, Xinyang; Jiang, Zhifan; Van Leemput, Koen; Piraud, Marie; Ezhov, Ivan; Johanson, Elaine; Meier, Zeke; Familiar, Ariana; Kazerooni, Anahita Fathi; Kofler, Florian; Calabrese, Evan; Aneja, Sanjay; Chiang, Veronica; Ikuta, Ichiro; Shafique, Umber; Memon, Fatima; Conte, Gian Marco; Bakas, Spyridon; Rudie, Jeffrey; Aboian, Mariam; Radiology and Imaging Sciences, School of MedicineClinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.Item Tractography of Porcine Meniscus Microstructure Using High-Resolution Diffusion Magnetic Resonance Imaging(Frontiers Media, 2022-05-10) Shen, Jikai; Zhao, Qi; Qi, Yi; Cofer, Gary; Johnson, G. Allan; Wang, Nian; Radiology and Imaging Sciences, School of MedicineTo noninvasively evaluate the three-dimensional collagen fiber architecture of porcine meniscus using diffusion MRI, meniscal specimens were scanned using a 3D diffusion-weighted spin-echo pulse sequence at 7.0 T. The collagen fiber alignment was revealed in each voxel and the complex 3D collagen network was visualized for the entire meniscus using tractography. The proposed automatic segmentation methods divided the whole meniscus to different zones (Red-Red, Red-White, and White-White) and different parts (anterior, body, and posterior). The diffusion tensor imaging (DTI) metrics were quantified based on the segmentation results. The heatmap was generated to investigate the connections among different regions of meniscus. Strong zonal-dependent diffusion properties were demonstrated by DTI metrics. The fractional anisotropy (FA) value increased from 0.13 (White-White zone) to 0.26 (Red-Red zone) and the radial diffusivity (RD) value changed from 1.0 × 10-3 mm2/s (White-White zone) to 0.7 × 10-3 mm2/s (Red-Red zone). Coexistence of both radial and circumferential collagen fibers in the meniscus was evident by diffusion tractography. Weak connections were found between White-White zone and Red-Red zone in each part of the meniscus. The anterior part and posterior part were less connected, while the body part showed high connections to both anterior part and posterior part. The tractography based on diffusion MRI may provide a complementary method to study the integrity of meniscus and nondestructively visualize the 3D collagen fiber architecture.Item Understanding metric-related pitfalls in image analysis validation(ArXiv, 2023-09-25) Reinke, Annika; Tizabi, Minu D.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A. Emre; Rädsch, Tim; Sudre, Carole H.; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Blaschko, Matthew B.; Buettner, Florian; Cardoso, M. Jorge; Cheplygina, Veronika; Chen, Jianxu; Christodoulou, Evangelia; Cimini, Beth A.; Collins, Gary S.; Farahani, Keyvan; Ferrer, Luciana; Galdran, Adrian; Van Ginneken, Bram; Glocker, Ben; Godau, Patrick; Haase, Robert; Hashimoto, Daniel A.; Hoffman, Michael M.; Huisman, Merel; Isensee, Fabian; Jannin, Pierre; Kahn, Charles E.; Kainmueller, Dagmar; Kainz, Bernhard; Karargyris, Alexandros; Karthikesalingam, Alan; Kenngott, Hannes; Kleesiek, Jens; Kofler, Florian; Kooi, Thijs; Kopp-Schneider, Annette; Kozubek, Michal; Kreshuk, Anna; Kurc, Tahsin; Landman, Bennett A.; Litjens, Geert; Madani, Amin; Maier-Hein, Klaus; Martel, Anne L.; Mattson, Peter; Meijering, Erik; Menze, Bjoern; Moons, Karel G. M.; Müller, Henning; Nichyporuk, Brennan; Nickel, Felix; Petersen, Jens; Rafelski, Susanne M.; Rajpoot, Nasir; Reyes, Mauricio; Riegler, Michael A.; Rieke, Nicola; Saez-Rodriguez, Julio; Sánchez, Clara I.; Shetty, Shravya; Summers, Ronald M.; Taha, Abdel A.; Tiulpin, Aleksei; Tsaftaris, Sotirios A.; Van Calster, Ben; Varoquaux, Gaël; Yaniv, Ziv R.; Jäger, Paul F.; Maier-Hein, Lena; Pathology and Laboratory Medicine, School of MedicineValidation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Item Utility of pre-procedural [99mTc]TcMAA SPECT/CT Multicompartment Dosimetry for Treatment Planning of 90Y Glass microspheres in patients with Hepatocellular Carcinoma: comparison of anatomic versus [99mTc]TcMAA-based Segmentation(Springer, 2025) Lam, Marnix; Garin, Etienne; Haste, Paul; Denys, Alban; Geller, Brian; Kappadath, S. Cheenu; Turkmen, Cuneyt; Sze, Daniel Y.; Alsuhaibani, Hamad Saleh; Herrmann, Ken; Maccauro, Marco; Cantasdemir, Murat; Dreher, Matthew; Fowers, Kirk D.; Gates, Vanessa; Salem, Riad; Radiology and Imaging Sciences, School of MedicinePurpose: Pre-treatment [99mTc]TcMAA-based radioembolization treatment planning using multicompartment dosimetry involves the definition of the tumor and normal tissue compartments and calculation of the prescribed absorbed doses. The aim was to compare the real-world utility of anatomic and [99mTc]TcMAA-based segmentation of tumor and normal tissue compartments. Materials and methods: Included patients had HCC treated by glass [90Y]yttrium microspheres, ≥ 1 tumor, ≥ 3 cm diameter and [99mTc]TcMAA SPECT/CT imaging before treatment. Segmentation was performed retrospectively using dedicated dosimetry software: (1) anatomic (diagnostic CT/MRI-based), and (2) [99mTc]TcMAA threshold-based (i.e., using an activity-isocontour threshold). CT/MRI was co-registered with [99mTc]TcMAA SPECT/CT. Logistic regression and Cox regression, respectively, were used to evaluate relationships between total perfused tumor absorbed dose (TAD) and objective response rate (ORR) and overall survival (OS). In a subset-analysis pre- and post-treatment dosimetry were compared using Bland-Altman analysis and Pearson's correlation coefficient. Results: A total of 209 patients were enrolled. Total perfused tumor and normal tissue volumes were larger when using anatomic versus [99mTc]TcMAA threshold segmentation, resulting in lower absorbed doses. mRECIST ORR was higher with increasing total perfused TAD (odds ratio per 100 Gy TAD increase was 1.22 (95% CI: 1.01-1.49; p = 0.044) for anatomic and 1.19 (95% CI: 1.04-1.37; p = 0.012) for [99mTc]TcMAA threshold segmentation. Higher total perfused TAD was associated with improved OS (hazard ratio per 100 Gy TAD increase was 0.826 (95% CI: 0.714-0.954; p = 0.009) and 0.847 (95% CI: 0.765-0.936; p = 0.001) for anatomic and [99mTc]TcMAA threshold segmentation, respectively). For pre- vs. post-treatment dosimetry comparison, the average bias for total perfused TAD was + 11.5 Gy (95% limits of agreement: -227.0 to 250.0) with a strong positive correlation (Pearson's correlation coefficient = 0.80). Conclusion: Real-world data support [99mTc]TcMAA imaging to estimate absorbed doses prior to treatment of HCC with glass [90Y]yttrium microspheres. Both anatomic and [99mTc]TcMAA threshold methods were suitable for treatment planning.Item Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans(Springer, 2018-01-05) Cong, Shan; Risacher, Shannon L.; West, John D.; Wu, Yu-Chien; Apostolova, Liana G.; Tallman, Eileen; Rizkalla, Maher; Salama, Paul; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of MedicineThe hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer’s disease (AD). The Automatic Segmentation of Hippocampal Subfields (ASHS) software was used to segment 9 primary labeled regions including hippocampal subfields and neighboring cortical regions. Intraclass correlation coefficients were computed for reliability tests between 4 and 8 min scans within and across the four groups. Pairwise group analyses were performed, covaried for age, sex and total intracranial volume, to determine whether the patterns of group differences were similar using 4 vs. 8 min scans. The 4 and 8 min protocols, analyzed by ASHS segmentation, yielded similar volumetric estimates for hippocampal subfields as well as comparable patterns of differences between study groups. The accelerated protocol can provide reliable imaging data for investigation of hippocampal subfields in AD-related MRI studies and the decreased scan time may result in less vulnerability to motion.