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Jaesung Lee

Bio: Jaesung Lee is an academic researcher from Cornell University. The author has contributed to research in topics: Intravital microscopy & Airway. The author has an hindex of 5, co-authored 9 publications receiving 271 citations.

Papers
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Journal ArticleDOI
TL;DR: A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
Abstract: This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate 15 different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

241 citations

Proceedings ArticleDOI
TL;DR: A fully automated algorithm to segment the individual ribs from low-dose chest CT scans by identifying the centerline of the spinal canal and growing each rib from the seed region and separated from the corresponding vertebra.
Abstract: Segmentation of individual ribs and other bone structures in chest CT images is important for anatomical analysis, as the segmented ribs may be used as a baseline reference for locating organs within a chest as well as for identification and measurement of any geometric abnormalities in the bone. In this paper we present a fully automated algorithm to segment the individual ribs from low-dose chest CT scans. The proposed algorithm consists of four main stages. First, all the high-intensity bone structure present in the scan is segmented. Second, the centerline of the spinal canal is identified using a distance transform of the bone segmentation. Then, the seed region for every rib is detected based on the identified centerline, and each rib is grown from the seed region and separated from the corresponding vertebra. This algorithm was evaluated using 115 low-dose chest CT scans from public databases with various slice thicknesses. The algorithm parameters were determined using 5 scans, and remaining 110 scans were used to evaluate the performance of the segmentation algorithm. The outcome of the algorithm was inspected by an author for the correctness of the segmentation. The results indicate that over 98% of the individual ribs were correctly segmented with the proposed algorithm.

33 citations

Journal ArticleDOI
TL;DR: In addition to identifying tissue-specific consequences of checkpoint dysfunction, these data highlight a robust, cooperative configuration for the mammalian DNA damage response network and further suggest HUS1 and related genes in the ATR pathway as candidate modifiers of disease severity in A-T patients.
Abstract: The human genomic instability syndrome ataxia telangiectasia (A-T), caused by mutations in the gene encoding the DNA damage checkpoint kinase ATM, is characterized by multisystem defects including neurodegeneration, immunodeficiency and increased cancer predisposition. ATM is central to a pathway that responds to double-strand DNA breaks, whereas the related kinase ATR leads a parallel signaling cascade that is activated by replication stress. To dissect the physiological relationship between the ATM and ATR pathways, we generated mice defective for both. Because complete ATR pathway inactivation causes embryonic lethality, we weakened the ATR mechanism to different degrees by impairing HUS1, a member of the 911 complex that is required for efficient ATR signaling. Notably, simultaneous ATM and HUS1 defects caused synthetic lethality. Atm/Hus1 double-mutant embryos showed widespread apoptosis and died mid-gestationally. Despite the underlying DNA damage checkpoint defects, increased DNA damage signaling was observed, as evidenced by H2AX phosphorylation and p53 accumulation. A less severe Hus1 defect together with Atm loss resulted in partial embryonic lethality, with the surviving double-mutant mice showing synergistic increases in genomic instability and specific developmental defects, including dwarfism, craniofacial abnormalities and brachymesophalangy, phenotypes that are observed in several human genomic instability disorders. In addition to identifying tissue-specific consequences of checkpoint dysfunction, these data highlight a robust, cooperative configuration for the mammalian DNA damage response network and further suggest HUS1 and related genes in the ATR pathway as candidate modifiers of disease severity in A-T patients.

17 citations

Proceedings ArticleDOI
06 Mar 2008
TL;DR: A method to assess theAirway dimensions from CT scans, including the airway segments that are not oriented axially, is introduced, which represents each airway segment using a segment-centric generalized cylinder model and assess airway lumen diameter and wall thickness for each segment by determining inner and outer wall boundaries.
Abstract: A wide range of pulmonary diseases, including common ones such as COPD, affect the airways. If the dimensions of airway can be measured with high confidence, the clinicians will be able to better diagnose diseases as well as monitor progression and response to treatment. In this paper, we introduce a method to assess the airway dimensions from CT scans, including the airway segments that are not oriented axially. First, the airway lumen is segmented and skeletonized, and subsequently each airway segment is identified. We then represent each airway segment using a segment-centric generalized cylinder model and assess airway lumen diameter (LD) and wall thickness (WT) for each segment by determining inner and outer wall boundaries. The method was evaluated on 14 healthy patients from a Weill Cornell database who had two scans within a 2 month interval. The corresponding airway segments were located in two scans and measured using the automated method. The total number of segments identified in both scans was 131. When 131 segments were considered altogether, the average absolute change over two scans was 0.31 mm for LD and 0.12 mm for WT, with 95% limits of agreement of [-0.85, 0.83] for LD and [-0.32, 0.26] for WT. The results were also analyzed on per-patient basis, and the average absolute change was 0.19 mm for LD and 0.05 mm for WT. 95% limits of agreement for per-patient changes were [-0.57, 0.47] for LD and [-0.16, 0.10] for WT.

14 citations

Journal ArticleDOI
TL;DR: A new feature-based tracking algorithm is presented for automatically measuring diameter of vessels in intravital video microscopy image sequences, which tracks the vessel diameter throughout the entire image sequence once the diameter is marked in the first image.
Abstract: The blood vessel diameter is often measured in microcirculation studies to quantify the effects of various stimuli. Intravital video microscopy is used to measure the change in vessel diameter by first recording the video and analyzing it using electronic calipers or by using image shearing technique. Manual measurement using electronic calipers or image shearing is time-consuming and prone to measurement error, and automated measurement can serve as an alternative that is faster and more reliable. In this paper, a new feature-based tracking algorithm is presented for automatically measuring diameter of vessels in intravital video microscopy image sequences. Our method tracks the vessel diameter throughout the entire image sequence once the diameter is marked in the first image. The parameters were calibrated using the intravital videos with manual ground truth measurements. The expriment with 10 synthetic videos and 20 intravital microscopy videos, including 10 fluorescence confocal and 10 non-confocal transmission, shows that the measurement can be performed accurately.

10 citations


Cited by
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Journal ArticleDOI
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

3,699 citations

Journal ArticleDOI
TL;DR: A historical perspective of their discovery is provided and their established functions in sensing and responding to genotoxic stress are discussed, as well as emerging non-canonical roles and how knowledge of ATM, ATR, and DNA-PK is being translated to benefit human health.

1,175 citations

Journal ArticleDOI
TL;DR: The recent findings and current models of how ATM and ATR senseDNA damage, how they are activated by DNA damage, and how they function in concert to regulate the DDR are discussed.
Abstract: In eukaryotic cells, maintenance of genomic stability relies on the coordinated action of a network of cellular processes, including DNA replication, DNA repair, cell-cycle progression, and others. The DNA damage response (DDR) signaling pathway orchestrated by the ATM and ATR kinases is the central regulator of this network in response to DNA damage. Both ATM and ATR are activated by DNA damage and DNA replication stress, but their DNA-damage specificities are distinct and their functions are not redundant. Furthermore, ATM and ATR often work together to signal DNA damage and regulate downstream processes. Here, we will discuss the recent findings and current models of how ATM and ATR sense DNA damage, how they are activated by DNA damage, and how they function in concert to regulate the DDR.

1,064 citations

Journal ArticleDOI
TL;DR: This paper introduces a robust, learning-based brain extraction system (ROBEX), which combines a discriminative and a generative model to achieve the final result and shows that ROBEX provides significantly improved performance measures for almost every method/dataset combination.
Abstract: Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. ROBEX was compared with six other popular, publicly available methods (BET, BSE, FreeSurfer, AFNI, BridgeBurner, and GCUT) on three publicly available datasets (IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination.

539 citations

Journal ArticleDOI
TL;DR: The classification system proposed and illustrated in this article provides a structured approach to visual and quantitative assessment of COPD and helps to contribute to a personalized approach to the treatment of patients with COPD.
Abstract: Integration of visual characterization of emphysema and airway abnormalities with physiologic and quantitative CT assessment permits categorization of chronic obstructive pulmonary disease into distinct structurally and functionally defined subtypes.

389 citations