Marcos Salganicoff

Siemens, München, Bavaria, Germany

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Publications (45)50.97 Total impact

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    ABSTRACT: Objectives To assess the effectiveness of computer-aided detection (CAD) as a second reader or concurrent reader in helping radiologists who are moderately experienced in computed tomographic colonography (CTC) to detect colorectal polyps. Methods Seventy CTC datasets (34 patients: 66 polyps ≥6 mm; 36 patients: no abnormalities) were retrospectively reviewed by seven radiologists with moderate CTC experience. After primary unassisted evaluation, a CAD second read and, after a time interval of ≥4 weeks, a CAD concurrent read were performed. Areas under the receiver operating characteristic (ROC) curve (AUC), along with per-segment, per-polyp and per-patient sensitivities, and also reading times, were calculated for each reader with and without CAD. Results Of seven readers, 86 % and 71 % achieved a higher accuracy (segment-level AUC) when using CAD as second and concurrent reader respectively. Average segment-level AUCs with second and concurrent CAD (0.853 and 0.864) were significantly greater (p < 0.0001) than average AUC in the unaided evaluation (0.781). Per-segment, per-polyp, and per-patient sensitivities for polyps ≥6 mm were significantly higher in both CAD reading paradigms compared with unaided evaluation. Second-read CAD reduced readers’ average segment and patient specificity by 0.007 and 0.036 (p = 0.005 and 0.011), respectively. Conclusions CAD significantly improves the sensitivities of radiologists moderately experienced in CTC for polyp detection, both as second reader and concurrent reader. Key Points • CAD helps radiologists with moderate CTC experience to detect polyps ≥6 mm. • Second and concurrent read CAD increase the radiologist’s sensitivity for detecting polyps ≥6 mm. • Second read CAD slightly decreases specificity compared with an unassisted read. • Concurrent read CAD is significantly more time-efficient than second read CAD.
    European Radiology 07/2014; 24(7). · 4.34 Impact Factor
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    Dijia Wu, Le Lu, Jinbo Bi, Marcos Salganicoff
    Ref. No: US 8724866 B2, Year: 05/2014
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  • Medical Physics. 07/2013;
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    Le Lu, Jun Ma, Marcos Salganicoff, Yiqiang Zhan, Xiang Sean Zhou
    Ref. No: US8437521, Year: 05/2013
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    ABSTRACT: The objective of our study was to evaluate the impact of computer-aided detection (CAD) on the identification of subsolid and solid lung nodules on thin- and thick-section CT. For 46 chest CT examinations with ground-glass opacity (GGO) nodules, CAD marks computed using thin data were evaluated in two phases. First, four chest radiologists reviewed thin sections (reader(thin)) for nodules and subsequently CAD marks (reader(thin) + CAD(thin)). After 4 months, the same cases were reviewed on thick sections (reader(thick)) and subsequently with CAD marks (reader(thick) + CAD(thick)). Sensitivities were evaluated. Additionally, reader(thick) sensitivity with assessment of CAD marks on thin sections was estimated (reader(thick) + CAD(thin)). For 155 nodules (mean, 5.5 mm; range, 4.0-27.5 mm)-74 solid nodules, 22 part-solid (part-solid nodules), and 59 GGO nodules-CAD stand-alone sensitivity was 80%, 95%, and 71%, respectively, with three false-positives on average (0-12) per CT study. Reader(thin) + CAD(thin) sensitivities were higher than reader(thin) for solid nodules (82% vs 57%, p < 0.001), part-solid nodules (97% vs 81%, p = 0.0027), and GGO nodules (82% vs 69%, p < 0.001) for all readers (p < 0.001). Respective sensitivities for reader(thick), reader(thick) + CAD(thick), reader(thick) + CAD(thin) were 40%, 58% (p < 0.001), and 77% (p < 0.001) for solid nodules; 72%, 73% (p = 0.322), and 94% (p < 0.001) for part-solid nodules; and 53%, 58% (p = 0.008), and 79% (p < 0.001) for GGO nodules. For reader(thin), false-positives increased from 0.64 per case to 0.90 with CAD(thin) (p < 0.001) but not for reader(thick); false-positive rates were 1.17, 1.19, and 1.26 per case for reader(thick), reader(thick) + CAD(thick), and reader(thick) + CAD(thin), respectively. Detection of GGO nodules and solid nodules is significantly improved with CAD. When interpretation is performed on thick sections, the benefit is greater when CAD marks are reviewed on thin rather than thick sections.
    American Journal of Roentgenology 01/2013; 200(1):74-83. · 2.90 Impact Factor
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    ABSTRACT: OBJECTIVES: To assess the performance of an advanced "first-reader" workflow for computer-aided detection (CAD) of colorectal adenomas ≥ 6 mm at computed tomographic colonography (CTC) in a low-prevalence cohort. METHODS: A total of 616 colonoscopy-validated CTC patient-datasets were retrospectively reviewed by a radiologist using a "first-reader" CAD workflow. CAD detections were presented as galleries of six automatically generated two-dimensional (2D) and three-dimensional (3D) images together with interactive 3D target views and 2D multiplanar views of the complete dataset. Each patient-dataset was interpreted by initially using CAD image-galleries followed by a fast 2D review to address unprompted colonic areas. Per-patient, per-polyp, and per-adenoma sensitivities were calculated for lesions ≥ 6 mm. Statistical testing employed Fisher's exact and McNemar tests. RESULTS: In 91/616 patients, 131 polyps (92 adenomas, 39 non-adenomas) ≥ 6 mm and two cancers were identified by reference standard. Using the CAD gallery-based first-reader workflow, the radiologist detected all adenomas ≥ 10 mm (34/34) and cancers. Per-patient and polyp sensitivities for lesions ≥ 6 mm were 84.3 % (75/89), and 83.2 % (109/131), respectively, with 89.1 % (57/64) and 85.9 % (79/92) for adenomas. Overall specificity was 95.6 % (504/527). Mean interpretation time was 3.1 min per patient. CONCLUSIONS: A CAD algorithm, applied in an image-gallery-based first-reader workflow, can substantially decrease reading times while enabling accurate detection of colorectal adenomas in a low-prevalence population. KEY POINTS : • Computer-aided detection (CAD) is increasingly used to help interpret CT colonography (CTC) • An image-gallery first-reader CAD-workflow is feasible for detection of colorectal adenomas ≥ 6 mm • Image-gallery first-reader CAD yields per-patient sensitivity of 89.1 % and specificity of 95.6 % • The mean reading time for CTC was 3.1 min, making screening feasible • No large adenoma was missed by the radiologist who reviewed with CAD galleries.
    European Radiology 08/2012; · 4.34 Impact Factor
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    ABSTRACT: The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.
    Journal of Digital Imaging 06/2012; · 1.10 Impact Factor
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    ABSTRACT: Purpose: To evaluate the stand-alone performance of a computer-aided detection (CAD) algorithm for colorectal polyps in a large heterogeneous CT colonography (CTC) database that included both tagged and untagged datasets. Methods: Written, informed consent was waived for this institutional review board-approved, HIPAA-compliant retrospective study. CTC datasets from 2063 patients were assigned to training (n = 374) and testing (n = 1689). The test set consisted of 836 untagged and 853 tagged examinations not used for CAD training. Examinations were performed at 15 sites in the United States, Asia, and Europe, using 4- to 64-multidetector-row computed tomography and various acquisition parameters. CAD sensitivities were calculated on a per-patient and per-polyp basis for polyps measuring ≥6 mm. The reference standard was colonoscopy in 1588 (94%) and consensus interpretation by expert radiologists in 101 (6%) patients. Statistical testing employed χ2, logistic regression, and Mann-Whitney U tests. Results: In 383 of 1689 individuals, 564 polyps measuring ≥6 mm were identified by the reference standard (347 polyps: 6–9 mm and 217 polyps: ≥10 mm). Overall, CAD per-patient sensitivity was 89.6% (343/383), with 89.0% (187/210) for untagged and 90.2% (156/173) for tagged datasets (P = 0.72). Overall, per-polyp sensitivity was 86.9% (490/564), with 84.4% (270/320) for untagged and 90.2% (220/244) for tagged examinations (P = 068). The mean false-positive rate per patient was 5.14 (median, 4) in untagged and 4.67 (median, 4) in tagged patient datasets (P = 0.353). Conclusion: Stand-alone CAD can be applied to both tagged and untagged CTC studies without significant performance differences. Detection rates are comparable to human readers at a relatively low false-positive rate, making CAD a useful tool in clinical practice.
    Investigative Radiology 01/2012; 47(2):99–108. · 5.46 Impact Factor
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    ABSTRACT: Pulmonary nodules are potential manifestations of lung cancer, and their detection and inspection are essential for screening and diagnosis of the disease. The growth of a nodule is considered one of the most important cues for assessing its malignancy. Hence, the ability to segment the nodules accurately and measure their growth over time is crucial for prognosis. Accurate nodule segmentation is also vital for drug therapy development. A segmentation that can provide a consistent, reproducible, and accurate volumetric measure of nodule shrinkage/growth is very critical for evaluating the effectiveness of drug treatments
    05/2011: pages 143-188;
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    ABSTRACT: Common chest CT clinical workflows for detecting lung nodules use a large slice thickness protocol (typically 5 mm). However, most existing CAD studies are performed on a thin slice data (0.3-2 mm) available on state-of-the art scanners. A major challenge for the widespread clinical use of Lung CAD is the concurrent availability of both thick and thin resolutions for use by radiologist and CAD respectively. Having both slice thickness reconstructions is not always possible based on the availability of scanner technologies, acquisition parameters chosen at remote site, and transmission and archiving constraints that may make transmission and storage of large data impracticable. However, applying current thin-slice CAD algorithms on thick slice cases outside their designed acquisition parameters may result in degradation of sensitivity and high false-positive rate making them clinically unacceptable. Therefore a CAD system that can handle thicker slice acquisitions is desirable to address those situations. In this paper, we propose a CAD system which works directly on thick slice scans. We first propose a multi-stage classifier based CAD system for detecting lung nodules in such data. Furthermore, we propose different gating systems adapted for thick slice scans. The proposed gating schemes are based on: 1. wall-attached and non wall-attached. 2. central and non-central region. These gating schemes can be used independently or combined as well. Finally, we present prototype1 results showing significant improvement of CAD sensitivity at much better false positive rate on thick-slice CT images are presented.
    Proc SPIE 03/2011;
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    ABSTRACT: Accurate segmentation of a pulmonary nodule is an important and active area of research in medical image processing. Although many algorithms have been reported in literature for this problem, those that are applicable to various density types have not been available until recently. In this paper, we propose a new algorithm that is applicable to solid, non-solid and part-solid types and solitary, vascularized, and juxtapleural types. First, the algorithm separates lung parenchyma and radiographically denser anatomical structures with coupled competition and diffusion processes. The technique tends to derive a spatially more homogeneous foreground map than an adaptive thresholding based method. Second, it locates the core of a nodule in a manner that is applicable to juxtapleural types using a transformation applied on the Euclidean distance transform of the foreground. Third, it detaches the nodule from attached structures by a region growing on the Euclidean distance map followed by a procedure to delineate the surface of the nodule based on the patterns of the region growing and distance maps. Finally, convex hull of the nodule surface intersected with the foreground constitutes the final segmentation. The performance of the technique is evaluated with two Lung Imaging Database Consortium (LIDC) data sets with 23 and 82 nodules each, and another data set with 820 nodules with manual diameter measurements. The experiments show that the algorithm is highly reliable in segmenting nodules of various types in a computationally efficient manner.
    Medical image analysis 02/2011; 15(1):133-54. · 3.09 Impact Factor
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    ABSTRACT: Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (“nodule ≥ 3 mm,” “nodule<3 mm,” and “non-nodule ≥ 3 mm”). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked “nodule” by at least one radiologist. 2669 of these lesions were marked “nodule ≥ 3 mm” by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
    Medical Physics 01/2011; 38(2):915-931. · 2.91 Impact Factor
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    ABSTRACT: In this chapter, we describe in detail algorithms that tackle specific clinical problems and that have been extensively validated in close collaboration with radiologists. Our hope is that its contents will be a useful overview of some of the problems that present themselves in real clinical practice and of some of the techniques that have proven to fulfill the challenging requirements of these problems in terms of speed and robustness, and that have become properly validated products.
    01/2011: pages 263-313; , ISBN: 978-1-4419-8204-9
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    Le Lu, Jinbo Bi, Matthias Wolf, Marcos Salganicoff
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    ABSTRACT: D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical im- ages of computer aided diagnosis (CAD). In this paper, we propose effective image segmentation features and a novel multiple instance regression method for solving the above challenges. We perform supervised learning based seg- mentation algorithm on numerous lesion candidates (as 3D VOIs: Volumes Of Interest in CT images) which can be true or false. By assessing the statistical properties in the joint space of segmentation output (e.g., a 3D class-specific prob- ability map or cloud), and original image appearance, 57 descriptive features in six subgroups are derived. The new feature set shows excellent performance on effectively clas- sifying ambiguous positive and negative VOIs, for our CAD system of detecting colonic polyps using CT images. The proposed regression model on our segmentation derived features behaves as a robust object (polyp) size/importance estimator and ranking module with high reliability, which is critical for automatic clinical reporting and cancer staging. Extensive evaluation is executed on a large clinical dataset of 770 CT scans from 12 medical sites for validation, with the best state-of-the-art results.
    The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 20-25 June 2011; 01/2011
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    Meizhu Liu, Le Lu, Xiaojing Ye, Shipeng Yu, Marcos Salganicoff
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    ABSTRACT: Classification is one of the core problems in computer-aided cancer diagnosis (CAD) via medical image interpretation. High detection sensitivity with reasonably low false positive (FP) rate is essential for any CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. In this paper, we propose a novel classification framework based on sparse representation. It first builds an overcomplete dictionary of atoms for each class via K-SVD learning, then classification is formulated as sparse coding which can be solved efficiently. This representation naturally generalizes for both binary and multiwise classification problems, and can be used as a standalone classifier or integrated with an existing decision system. Our method is extensively validated in CAD systems for both colorectal polyp and lung nodule detection, using hospital scale, multi-site clinical datasets. The results show that we achieve superior classification performance than existing state-of-the-arts, using support vector machine (SVM) and its variants, boosting, logistic regression, relevance vector machine (RVM), or kappa-nearest neighbor (KNN).
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 3):41-8.
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    ABSTRACT: To evaluate the effect of a computer-aided detection (CAD) algorithm on the performance of novice readers for detection of pulmonary embolism (PE) at CT pulmonary angiography (CTPA). We included CTPA examinations of 79 patients (50 female, 52 ± 18 years). Studies were evaluated by two independent inexperienced readers who marked all vessels containing PE. After 3 months all studies were reevaluated by the same two readers, this time aided by CAD prototype. A consensus read by three expert radiologists served as the reference standard. Statistical analysis used χ(2) and McNemar testing. Expert consensus revealed 119 PEs in 32 studies. For PE detection, the sensitivity of CAD alone was 78%. Inexperienced readers' initial interpretations had an average per-PE sensitivity of 50%, which improved to 71% (p < 0.001) with CAD as a second reader. False positives increased from 0.18 to 0.25 per study (p = 0.03). Per-study, the readers initially detected 27/32 positive studies (84%); with CAD this number increased to 29.5 studies (92%; p = 0.125). Our results suggest that CAD significantly improves the sensitivity of PE detection for inexperienced readers with a small but appreciable increase in the rate of false positives.
    European Radiology 01/2011; 21(6):1214-23. · 4.34 Impact Factor
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    ABSTRACT: Computer aided detection (CAD) systems have emerged as noninvasive and effective tools, using 3D CT Colonography (CTC) for early detection of colonic polyps. In this paper, we propose a robust and automatic polyp prone-supine view matching method, to facilitate the regular CTC workflow where radiologists need to manually match the CAD findings in prone and supine CT scans for validation. Apart from previous colon registration approaches based on global geometric information, this paper presents a feature selection and metric distance learning approach to build a pairwise matching function (where true pairs of polyp detections have smaller distances than false pairs), learned using local polyp classification features. Thus our process can seamlessly handle collapsed colon segments or other severe structural artifacts which often exist in CTC, since only local features are used, whereas other global geometry dependent methods may become invalid for collapsed segmentation cases. Our automatic approach is extensively evaluated using a large multi-site dataset of 195 patient cases in training and 223 cases for testing. No external examination on the correctness of colon segmentation topology is needed. The results show that we achieve significantly superior matching accuracy than previous methods, on at least one order-of-magnitude larger CTC datasets.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 3):75-82.
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    ABSTRACT: Precise segmentation and identification of thoracic vertebrae is important for many medical imaging applications whereas it remains challenging due to vertebra’s complex shape and varied neighboring structures. In this paper, a new method based on learned bone-structure edge detectors and a coarse-to-fine deformable surface model is proposed to segment and identify vertebrae in 3D CT thoracic images. In the training stage, a discriminative classifier for object-specific edge detection is trained using steerable features and statistical shape models for 12 thoracic vertebrae are also learned. In the run-time, we design a new coarse-to-fine, two-stage segmentation strategy: subregions of a vertebra first deforms together as a group; then vertebra mesh vertices in a smaller neighborhood move group-wise, to progressively drive the deformable model towards edge response maps by optimizing a probability cost function. In this manner, the smoothness and topology of vertebra’s shapes are guaranteed. This algorithm performs successfully with reliable mean point-to-surface errors 0.95 ±0.91 mm on 40 volumes. Consequently a vertebra identification scheme is also proposed via mean surface meshes matching. We achieve a success rate of 73.1% using a single vertebra, and over 95% for 8 or more vertebra which is comparable or slightly better than state-of-the-art [1].
    Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I; 10/2010
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    Le Lu, Matthias Wolf, Jinbo Bi, Marcos Salganicoff
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    ABSTRACT: Ileo-Cecal Valve (ICV) is an important small soft organ which appears in human abdomen CT scans and connects colon and small intestine. Automated detection of ICV is of great clinical value for removing false positive (FP) findings in computer aided diagnosis (CAD) of colon cancers using CT colongraphy (CTC) [1,2,3]. However full 3D object detection, especially for small objects with large shape and pose variations as ICV, is very challenging. The final spatial detection accuracy often trades for robustness to find instances under variable conditions [4]. In this paper, we describe two significant post-parsing processes after the normal procedure of object (e.g., ICV) detection [4], to probabilistically interpret multiple hypotheses detections. It achieves nearly 300% performance improvement on (polyp detection) FP removal rate of [4], with about 1% extra computional overhead. First, a new multiple detection spatial-fusion method utilizes the initial single detection as an anchor identity and iteratively integrates other “trustful” detections by maximizing their spatial gains (if included) in a linkage. The ICV detection output is thus a set of N spatially connected boxes instead of a single box as top candidate, which allows to correct 3D detection misalignment inaccuracy. Next, we infer the spatial relationship between CAD generated polyp candidates and the detected ICV bounding boxes in 3D volume, and convert as a set of continuous valued, ICV-association features per candidate which allows further statistical analysis and classification for more rigorous false positive deduction in colon CAD. Based on our annotated 116 training cases, the spatial coverage ratio between the new N-box ICV detection and annotation is improved by 13.0% (N=2) and 19.6% (N=3) respectively. An evaluation on large scale datasets of total ~1400 CTC volumes, with different tagging preparations, reports average 5.1 FP candidates are removed at Candidate-Generation stage per scan; and the final CAD system mean FP rate drops from 2.2 to 1.82 per volume, without affecting the sensitivity.
    Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging - International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers; 01/2010

Publication Stats

257 Citations
50.97 Total Impact Points

Institutions

  • 2012
    • Siemens
      München, Bavaria, Germany
  • 2011
    • University of Florida
      Gainesville, Florida, United States
    • Susquehanna University
      Selinsgrove, Pennsylvania, United States
  • 2010
    • Johns Hopkins University
      Baltimore, Maryland, United States