Computer-Aided Detection of Mediastinal Lymph Nodes using Simple Architectural Convolutional Neural Network

Effect of CAD on Radiologists' Detection of Lung Nodules on Thoracic CT Scans: Analysis of an Observer Performance Study by Nodule Size

One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image

Artificial intelligence in breast imaging

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms

The objective of this study was to evaluate the performance of a computer-aided detection (CAD) system for the detection of breast cancer, based on mammographic appearance and histopathology. From 1000 consecutive screening mammograms from women with biopsy-proven breast carcinoma, 273 mammograms were selected randomly for retrospective evaluation by CAD. The sensitivity of the CAD system for breast cancer was assessed from the proportion of masses and microcalcifications detected. The corresponding tumor histopathologies also were evaluated. Normal mammograms (n = 155 patients) were used to determine the false-positive rate of the system. Of the 273 breast carcinomas, 149 appeared mammographically as masses, and 88 appeared as microcalcifications, including 36 carcinomas that presented as mixed lesions. The CAD system marked 125 of 149 masses correctly (84%), marked 86 of 88 microcalcifications correctly (98%), and marked 32 of 36 of mixed lesions correctly (89%.). The system showed a high sensitivity for the detection of ductal carcinoma in situ (95%; 73 of 77 lesions), invasive lobular carcinoma (95%; 18 of 19 lesions), invasive ductal carcinoma (85%; 125 of 147 lesions), and invasive mammary carcinoma (90%; 27 of 30 lesions). The highest CAD system sensitivity was for all invasive carcinomas that presented as microcalcifications (100%). On normal mammograms, there was an average of 1.3 false-positive CAD marks per image. The CAD system correctly marked a large majority of biopsy-proven breast cancers, with a greater sensitivity for lesions with microcalcifications and without significant impact of performance based on tumor histopathology. CAD was highly effective in detecting invasive lobular carcinoma (sensitivity, 95%) and ductal carcinoma in situ (sensitivity, 95%). CAD represents a useful tool for the detection of breast cancer.

This study evaluated the impact of differing false positive (FP) rates in two computer-aided detection (CADe) systems on the clinical effectiveness of artificial intelligence (AI)-assisted colonoscopy. The primary outcomes were adenoma detection rate (ADR) and adenomas per colonoscopy (APC). The ADR in the control, system A (3.2% FP rate), and system B (0.6% FP rate) groups were 44.3%, 43.4%, and 50.4%, respectively, with system B showing a significantly higher ADR than the control group. The APC for the control, A, and B groups were 0.75, 0.83, and 0.90, respectively, with system B also showing a higher APC than the control. The non-true lesion resection rates were 23.8%, 29.2%, and 21.3%, with system B having the lowest. The system with lower FP rates demonstrated improved ADR and APC without increasing the resection of non-neoplastic lesions. These findings suggest that higher FP rates negatively affect the clinical performance of AI-assisted colonoscopy.

ObjectiveTo evaluate the effect of computer-aided detection (CAD) system on observer performance in the detection of malignant lung nodules on chest radiograph.Materials and MethodsTwo hundred chest radiographs (100 normal and 100 abnormal with malignant solitary lung nodules) were evaluated. With CT and histological confirmation serving as a reference, the mean nodule size was 15.4 mm (range, 7-20 mm). Five chest radiologists and five radiology residents independently interpreted both the original radiographs and CAD output images using the sequential testing method. The performances of the observers for the detection of malignant nodules with and without CAD were compared using the jackknife free-response receiver operating characteristic analysis.ResultsFifty-nine nodules were detected by the CAD system with a false positive rate of 1.9 nodules per case. The detection of malignant lung nodules significantly increased from 0.90 to 0.92 for a group of observers, excluding one first-year resident (p = 0.04). When lowering the confidence score was not allowed, the average figure of merit also increased from 0.90 to 0.91 (p = 0.04) for all observers after a CAD review. On average, the sensitivities with and without CAD were 87% and 84%, respectively; the false positive rates per case with and without CAD were 0.19 and 0.17, respectively. The number of additional malignancies detected following true positive CAD marks ranged from zero to seven for the various observers.ConclusionThe CAD system may help improve observer performance in detecting malignant lung nodules on chest radiographs and contribute to a decrease in missed lung cancer.

To evaluate the efficacy of a new automatic computer-aided detection (CAD) system for mass screening of retinal nerve fibre layer (RNFL) defects in a large population using fundus photographs. Among the fundus photographs of 1200 consecutive subjects who visited a healthcare centre, a total of 2270 photographs appropriate for analysis were tested. The photographs were first reviewed by two expert ophthalmologists for detection of RNFL defects (gold standard manual detection). The images were then analysed using an automatic CAD system for detecting RNFL defects in various cases of glaucomatous and non-glaucomatous optic neuropathy. A free-response receiver operating characteristics curve was generated to evaluate the validity of the CAD system. The results of the automatic detection were compared with those of manual detection, and sensitivity and specificity of the CAD system were calculated. In manual detection of 2270 photographs, 41 RNFL defects from 36 photographs (1.6%) were detected, and no RNFL defects were found in 2234 photographs (98.4%). The sensitivity of the CAD system for detecting RNFL defects was 90.2% (37/41 RNFL defects) and the specificity was 72.5% (1620/2234 photographs with no RNFL defects) at a false-positive rate of 0.36 per image. The new CAD system successfully detected RNFL defects during mass screening of fundus photographs in a large population who visited a healthcare centre. The proposed algorithm can be useful for clinicians in screening RNFL defects in healthcare centres. The false-positive rate is still unsatisfactory, although improved compared with the previous study. Further studies are needed to enhance the speed and specificity of image analysis using the CAD system.

Background Interstitial lung abnormalities (ILAs) are present at CT in about 10% of individuals undergoing lung cancer screening. The relationship between histologic findings of ILAs and their influence on patient prognosis remains unknown. Purpose To evaluate the percentage of ILAs at preoperative CT, as measured by radiologists and a computer-aided detection (CAD) software, as a predictor of disease-free survival in patients with lung cancer. Materials and Methods This retrospective study evaluated 217 consecutive patients who underwent complete resection of lung cancer from April 2010 to December 2015. Two radiologists, blinded to the patients' clinical information, scored percentage fibrosis extent and determined whether ILAs and the usual interstitial pneumonia (UIP) pattern were present. They assessed ILA progression at follow-up CT. Two pathologists determined the presence of an UIP pattern. Percentage fibrosis extent was also measured by using CAD. Binary logistic regression analysis was performed to determine whether the CAD percentage fibrosis extent was associated with ILA at CT. Multivariable Cox regression analysis was used to evaluate the significance of CAD percentage fibrosis extent as a predictor of prognosis. Results The radiologists classified 47 patients with ILAs and 24 patients with a UIP pattern at chest CT. The pathologists detected a UIP pattern in 25 patients. CT abnormalities showed progression over a 2-year period in all patients with histologic evidence of UIP. After adjustment for age, sex, and smoking index, the CAD percentage fibrosis extent was independently associated with ILA (odds ratio: 3.1; 95% confidence interval [CI]: 2.1, 4.7; P < .001). After adjustment for age, forced expiratory volume in 1 second (percentage predicted) radiologist-assessed percentage fibrosis extent, and pathologic stage, CAD percentage fibrosis extent was independently associated with worse disease-free survival (hazard ratio: 1.3; 95% CI: 1.1, 1.6; P < .001). Conclusion Greater computer-aided detection percentage fibrosis extent at preoperative CT independently predicted worse disease-free survival in patients with lung cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Goo in this issue.

The main objective of this paper is to evaluate the performance of the Computer-Aided Detection (CAD) system for automated nodule detection in lungs using CT scan images. The CAD system is applied to CT scans collected in a screening program for lung cancer detection. Each scan consists of a sequence of about 300 slices stored in DICOM (Digital Imaging and Communications in Medicine) format. All true nodules were detected and a very low false-positive detection rate was achieved. The automated extraction of the pulmonary parenchyma in CT images is the most important step in a CAD system. In this paper we describe a method, consisting of techniques which are helpful for the automatic identification of the pulmonary volume. The performance is evaluated as a fully automated computerized method for the detection of lung nodules in computed tomography (CT) scans

BackgroundCADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings.MethodsThe purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives.ResultsThe database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant.Conclusions From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.

Development and validation of artificial neural networks model for detection of Barrett’s neoplasia: a multicenter pragmatic nonrandomized trial (with video)

We are developing a computer-aided detection (CAD) system for automated detection of ureteral wall thickening on multi-detector row CT urography, which potentially can assist radiologists in detecting ureter cancer. In the first stage of our CAD system, given a starting point, the ureter is tracked based on the CT values of the contrast-filled lumen. In the second stage, the ureter wall is segmented and the ureter wall thickness is estimated based on polar transformation, separation of the ureter wall from the background and measuring the wall thickness. In this pilot study, a limited data set of 20 patients with 22 abnormal ureters was used. Fourteen patients had a total of 16 ureters with malignant ureteral wall thickening. Two of the patients had malignant wall thickening in both the left and right ureters. The other six patients had 6 ureters with benign ureteral wall thickening. All malignant wall thickenings were biopsy-proven. The benign thickenings were determined by biopsy or by 2-year follow-up. In addition 3 normal ureters were used to determine the false positive (FP) detection rate of the CAD system. The tracking program successfully tracked the 25 ureters (22 abnormal and 3 normal) and detected 90% (20/22) of the ureters having wall thickening with 2.3 (7/3) FPs per ureter. 93% (15/16) of the ureters with malignant wall thickening and 83% (5/6) of the ureters with benign wall thickening were detected. The missed ureteral wall thickenings were developed asymmetrically around the part of the ureter filled with contrast and the detection criteria in our current CAD system was not able to identify them reliably. The preliminary results show that our detection system can track the ureter and can detect ureteral wall thickening.