Automatic Lumbar Spondylolisthesis Measurement in CT Images.

Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by the anterior shift of a lumbar vertebrae relative to subjacent vertebrae. In current clinical practices, staging of spondylolisthesis is often conducted in a qualitative way. Although meyerding grading opens the door to stage spondylolisthesis in a more quantitative way, it relies on the manual measurement, which is time consuming and irreproducible. Thus, an automatic measurement algorithm becomes desirable for spondylolisthesis diagnosis and staging. However, there are two challenges. 1) Accurate detection of the most anterior and posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small size of the vertebrae, slight errors of detection may lead to significant measurement errors, hence, wrong disease stages. 2) Automatic localize and label each lumbar vertebrae is required to provide the semantic meaning of the measurement. It is difficult since different lumbar vertebraes have high similarity of both shape and image appearance. To resolve these challenges, a new auto measurement framework is proposed with two major contributions: First, a learning based spine labeling method that integrates both the image appearance and spine geometry information is designed to detect lumbar vertebrae. Second, a hierarchical method using both the population information from atlases and domain-specific information in the target image is proposed for most anterior and posterior points positioning. Validated on 258 CT spondylolisthesis patients, our method shows very similar results to manual measurements by radiologists and significantly increases the measurement efficiency.

CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.

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.

Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT.

OBJECTIVE: 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. MATERIALS AND METHODS: 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)). RESULTS: 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. CONCLUSION: 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.

Time-efficient CT colonography interpretation using an advanced image-gallery-based, computer-aided "first-reader" workflow for the detection of colorectal adenomas.

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.

Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

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.

Computer-aided detection of colorectal polyps in CT colonography with and without fecal tagging: a stand-alone evaluation.

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 χ, 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.

Sparse classification for computer aided diagnosis using learned dictionaries.

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).

Robust large scale prone-supine polyp matching using local features: a metric learning approach.

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.

Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers?

PURPOSE: 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). MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models.

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.

Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model.

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.

Multi-level ground glass nodule detection and segmentation in CT lung images.

Early detection of Ground Glass Nodule (GGN) in lung Computed Tomography (CT) images is important for lung cancer prognosis. Due to its indistinct boundaries, manual detection and segmentation of GGN is labor-intensive and problematic. In this paper, we propose a novel multi-level learning-based framework for automatic detection and segmentation of GGN in lung CT images. Our main contributions are: firstly, a multi-level statistical learning-based approach that seamlessly integrates segmentation and detection to improve the overall accuracy for GGN detection (in a subvolume). The classification is done at two levels, both voxel-level and object-level. The algorithm starts with a three-phase voxel-level classification step, using volumetric features computed per voxel to generate a GGN class-conditional probability map. GGN candidates are then extracted from this probability map by integrating prior knowledge of shape and location, and the GGN object-level classifier is used to determine the occurrence of the GGN. Secondly, an extensive set of volumetric features are used to capture the GGN appearance. Finally, to our best knowledge, the GGN dataset used for experiments is an order of magnitude larger than previous work. The effectiveness of our method is demonstrated on a dataset of 1100 subvolumes (100 containing GGNs) extracted from about 200 subjects.

A two-level approach towards semantic colon segmentation: removing extra-colonic findings.

Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.

Computer-aided detection of pulmonary embolism: influence on radiologists' detection performance with respect to vessel segments.

The purpose was to assess the sensitivity of a CAD software prototype for the detection of pulmonary embolism in MDCT chest examinations with regard to vessel level and to assess the influence on radiologists' detection performance. Forty-three patients with suspected PE were included in this retrospective study. MDCT chest examinations with a standard PE protocol were acquired at a 16-slice MDCT. All patient data were read by three radiologists (R1, R2, R3), and all thrombi were marked. A CAD prototype software was applied to all datasets, and each finding of the software was analyzed with regard to vessel level. The standard of reference was assessed in a consensus read. Sensitivity for the radiologists and CAD software was assessed. Thirty-three patients were positive for PE, with a total of 215 thrombi. The mean overall sensitivity for the CAD software alone was 83% (specificity, 80%). Radiologist sensitivity was 77% = R3, 82% = R2, and R1 = 87%. With the aid of the CAD software, sensitivities increased to 98% (R1), 93% (R2), and 92% (R3) (p<0.0001). CAD performance at the lobar level was 87%, at the segmental 90% and at the subsegmental 77%. With the use of CAD for PE, the detection performance of radiologists can be improved.

Clinical evaluation of a computer-aided diagnosis (CAD) prototype for the detection of pulmonary embolism.

RATIONALE AND OBJECTIVES: To evaluate the performance of a prototype computer-aided diagnosis (CAD) tool using artificial intelligence techniques for the detection of pulmonary embolism (PE) and the possible benefit for general radiologists. MATERIALS AND METHODS: Forty multidetector row computed tomography datasets (16/64- channel scanner) using 100 kVp, 100 mAs effective/slice, and 1-mm axial reformats in a low-frequency reconstruction kernel were evaluated. A total of 80 mL iodinated contrast material was injected at a flow rate of 5 mL/seconds. Primarily, six general radiologists marked any PE using a commercially available lung evaluation software with simultaneous, automatic processing by CAD in the background. An expert panel consisting of two chest radiologists analyzed all PE marks from the readers and CAD, also searching for additional finding primarily missed by both, forming the ground truth. RESULTS: The ground truth consisted of 212 emboli. Of these, 65 (31%) were centrally and 147 (69%) were peripherally located. The readers detected 157/212 emboli (74%) leading to a sensitivity of 97% (63/65) for central and 70% (103/147) for peripheral emboli with 9 false-positive findings. CAD detected 168/212 emboli (79%), reaching a sensitivity of 74% for central (48/65) and 82%(120/147) for peripheral emboli. A total of 154 CAD candidates were considered as false positives, yielding an average of 3.85 false positives/case. CONCLUSIONS: The CAD software showed a sensitivity comparable to that of the general radiologists, but with more false positives. CAD detection of findings incremental to the radiologists suggests benefit when used as a second reader. Future versions of CAD have the potential to further increase clinical benefit by improving sensitivity and reducing false marks.

Automated volumetry of solid pulmonary nodules in a phantom: accuracy across different CT scanner technologies.

OBJECTIVES: The accuracy of automated volumetry for pulmonary nodules in a phantom using different CT scanner technologies from single-slice spiral CT (SSCT) to 64-slice multidetector-row CT (MDCT) was compared. MATERIALS AND METHODS: A lung phantom with 5 different categories of pulmonary nodules was scanned using a single-slice spiral CT, a 4-slice MDCT, a 16-slice MDCT and a 64-slice MDCT. Each category comprised of 7-9 nodules each (total n = 40) with different known volumes. Standard dose and low dose protocols were performed using thin and thick collimation. Image data were reconstructed at the thinnest slice thickness. Data sets were analyzed with a dedicated volumetry software. Volumes of all nodules were calculated and compared. RESULTS: Mean absolute percentage error (APE) for all nodules was 8.65% (+/-7.29%) for the SSCT, 10.26% (+/-8.25%) for the 4-slice MDCT, 8.19% (+/-7.57%) for the 16-slice MDCT and 7.89% (+/-7.39%) for the 64-slice MDCT. There was statistically significant influence of the scanner type, protocol, anatomic location, and nodule volume on APE, but overall, APEs were comparable. CONCLUSION: Computer-aided volumetry showed accurate measurements in all tested scanner types. This finding has important implications for nodule assessment and follow-up.

Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners.

The purpose of this study was to compare the accuracy of an automated volumetry software for phantom pulmonary nodules across various 16-slice multislice spiral CT (MSCT) scanners from different vendors. A lung phantom containing five different nodule categories (intraparenchymal, around a vessel, vessel attached, pleural, and attached to the pleura), with each category comprised of 7-9 nodules (total, n = 40) of varying sizes (diameter 3-10 mm; volume 6.62 mm(3)-525 mm(3)), was scanned with four different 16-slice MSCT scanners (Siemens, GE, Philips, Toshiba). Routine and low-dose chest protocols with thin and thick collimations were applied. The data from all scanners were used for further analysis using a dedicated prototype volumetry software. Absolute percentage volume errors (APE) were calculated and compared. The mean APE for all nodules was 8.4% (+/-7.7%) for data acquired with the 16-slice Siemens scanner, 14.3% (+/-11.1%) for the GE scanner, 9.7% (+/-9.6%) for the Philips scanner and 7.5% (+/-7.2%) for the Toshiba scanner, respectively. The lowest APEs were found within the diameter size range of 5-10 mm and volumes >66 mm(3). Nodule volumetry is accurate with a reasonable volume error in data from different scanner vendors. This may have an important impact for intraindividual follow-up studies.