Automatic segmentation of kidney and liver tumors in CT images

Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have chosen MICCAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores

인공지능 기반 임상의학 결정 지원 시스템 의료기기의 성능 및 안전성 검증을 위한 간 종양 표준 데이터셋 구축

Purpose To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30–50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learningbased AI algorithm for CDSS.ope

Sistem dapatan semula imej untuk aplikasi perubatan

Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus dalam sesuatu imej. Dalam bidang perubatan, sistem DSI digunakan untuk menyediakan imej yang diperlukan secara tepat dan pantas kepada pakar perubatan. Proses itu biasanya berlaku pada dan ketika diagnosis dan rawatan penyakit dilakukan. Sistem dapatan semula yang awal dan masih digunakan dengan meluas dalam bidang perubatan adalah sistem DSI berdasarkan teks (TBIRS). TBIRS menggunakan kata kunci dalam konteks sesuatu imej dan ia memerlukan anotasi teks secara manual. Proses anotasi teks adalah tugas yang memerihkan lebih-lebih lagi jika melibatkan pangkalan data yang besar. Ini memungkinkan kebarangkalian berlakunya kesilapan manusia adalah tinggi. Untuk mengatasi masalah yang dinyatakan, sistem DSI berdasarkan kandungan (CBIRS) dengan pengindeksan automatik adalah dicadangkan. Kaedah ini melibatkan pemprosesan imej perubatan berdasarkan komputer yang menggunakan fitur visual imej seperti warna, bentuk dan tesktur. Namun begitu, umum mengetahui bahawa suatu algoritma tertentu dalam CBIRS adalah khusus untuk satu modaliti sahaja dan melibatkan bahagian yang tertentu. Ini ditambahkan pula bahawa CBIRS telah mengabaikan persepsi manusia dalam tugas menakrif sesuatu imej dan akibatnya, menyebabkan wujudnya masalah jurang semantik. Oleh itu, sistem DSI hibrid (HBIRS) yang menggabungkan kekuatan kedua-dua TBIRS dan CBIRS telah diperkenalkan bagi menangani masalah jurang semantik khususnya dan sekaligus memantapkan sistem DSI amnya. Satu kerangka sistem DSI yang cekap iaitu HBIRS juga telah dicadangkan. Walau bagaimanapun, kajian ini hanya melibatkan TBIRS dan CBIRS bagi aplikasi perubatan, dan prototaip TBIRS yang dikaji menggunakan imej X-Ray turut dicadangkan

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis

Electrophysiologic assessment of (central) auditory processing disorder in children with non-syndromic cleft lip and/or palate

Session 5aPP - Psychological and Physiological Acoustics: Auditory Function, Mechanisms, and Models (Poster Session)Cleft of the lip and/or palate is a common congenital craniofacial malformation worldwide, particularly non-syndromic cleft lip and/or palate (NSCL/P). Though middle ear deficits in this population have been universally noted in numerous studies, other auditory problems including inner ear deficits or cortical dysfunction are rarely reported. A higher prevalence of educational problems has been noted in children with NSCL/P compared to craniofacially normal children. These high level cognitive difficulties cannot be entirely attributed to peripheral hearing loss. Recently it has been suggested that children with NSCLP may be more prone to abnormalities in the auditory cortex. The aim of the present study was to investigate whether school age children with (NSCL/P) have a higher prevalence of indications of (central) auditory processing disorder [(C)APD] compared to normal age matched controls when assessed using auditory event-related potential (ERP) techniques. School children (6 to 15 years) with NSCL/P and normal controls with matched age and gender were recruited. Auditory ERP recordings included auditory brainstem response and late event-related potentials, including the P1-N1-P2 complex and P300 waveforms. Initial findings from the present study are presented and their implications for further research in this area —and clinical intervention—are outlined. © 2012 Acoustical Society of Americapublished_or_final_versio

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 240, January 1983

Reports, articles and other documents, numbering 357, introduced into the NASA scientific and technical information system in December 1982 are given