Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts.

BACKGROUND: Ultrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US. METHODS: We constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images. RESULTS: The diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%-69.1%) and 47.3% (45.5%-47.3%) by human experts and non-experts, respectively. CONCLUSION: The performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.

Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection.

This chapter proposes a method to detect metastatic liver cancer from X-ray CT images using a convolutional neural network (CNN). The proposed method generates various lesion images by the combination of three kinds of generation methods: (1) synthesis using Poisson Blending, (2) generation based on CT value distributions, and (3) generation using deep convolutional generative adversarial networks (DCGANs). The proposed method constructs two kinds of detectors by using synthetic (fake) lesion images generated by the methods as well as real ones. One of the detectors is a 2D CNN for detecting candidate regions in a CT image, and the other is a 3D CNN for validating the candidate regions. Experimental results showed that the proposed method gave 0.30 improvement from 0.65 to 0.95 in terms of the detection rate, and 0.70 improvement from 0.90 to 0.20 in terms of the number of false detections per case. From the results, we confirmed the effectiveness of the proposed method.

Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spectrum.

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations.

Evaluation of a prostate biopsy strategy for cancer detection using a computer simulation system with virtual needle biopsy for three-dimensional prostate models.

AIM: We evaluated a prostate biopsy strategy for cancer detection using a computer simulation system with virtual needle biopsy for three-dimensional (3D) prostate models. METHODS: Two 3D prostate models with a volume of 25 cc or 50 cc were constructed from computed tomographic images of radical prostatectomy specimens. The peripheral zone (PZ) and transition zone (TZ) were arranged in the prostate models according to the anatomical information. Four thousand patterns of cancer lesions were automatically inserted into each prostate model with a proportion of 75% in PZ and 25% in TZ. Average hit rates (AHR) in prostate models were evaluated both with an increased number of biopsy cores and various angles of virtual needle biopsy. The influence of adding secondary tumors for hit rates was also evaluated. RESULTS: For both sizes, the laterally angled biopsy in 4-8 core biopsy schemes showed higher AHR than the vertical plane biopsy, while the vertical plane biopsy in 10-18 core biopsy schemes showed higher AHR than the laterally angled biopsy. A higher number of biopsy cores increased the AHR of secondary tumors. CONCLUSIONS: Our results suggest that it is important in prostate cancer detection to change the needle placement according to the number of biopsy cores and the size of the prostate.

Automated nomenclature of bronchial branches extracted from CT images and its application to biopsy path planning in virtual bronchoscopy.

We propose a novel anatomical labeling algorithm for bronchial branches extracted from CT images. This method utilizes multiple branching models for anatomical labeling. In the actual labeling process, the method selects the best candidate models at each branching point. Also a special labeling procedure is proposed for the right upper lobe. As an application of the automated nomenclature of bronchial branches, we utilized anatomical labeling results for assisting biopsy planning. When a user inputs a target point around suspicious regions on the display of a virtual bronchoscopy (VB) system, the path to the desired position is displayed as a sequence of anatomical names of branches. We applied the proposed method to 25 cases of CT images. The labeling accuracy was about 90%. Also the paths to desired positions were generated by using anatomical names in VB.