Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network.

Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing AD biomarkers extracted from brain images, adapt to different domain datasets, and accurately classify subjects with improved fine-tuning method. The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification in target domain. In this paper, deep supervision algorithm is used to improve the performance of already proposed 3D Adaptive CNN. Experiments on the ADNI MRI dataset without skull-stripping preprocessing have shown that the proposed 3D Deeply Supervised Adaptable CNN outperforms several proposed approaches, including 3D-CNN model, other CNN-based methods and conventional classifiers by accuracy and robustness. Abilities of the proposed network to generalize the features learnt and adapt to other domains have been validated on the CADDementia dataset.

CADBOSS: A computer-aided diagnosis system for whole-body bone scintigraphy scans.

AIMS: The aim of this study is to develop a computer-aided diagnosis system for bone scintigraphy scans. (CADBOSS). CADBOSS can detect metastases with a high success rates. The primary purpose of CADBOSS is as supplementary software to facilitate physician's decision making. MATERIALS AND METHODS: CADBOSS consists of various elements, such as hotspot segmentation, feature extraction/selection and classification. A level set active contour segmentation algorithm was used for the detection of hotspots. Moreover, a novel image gridding method was proposed for feature extraction of metastatic regions. An artificial neural network classifier was used to determine whether metastases were present. Performance evaluation of CADBOSS was performed with the help of an image database which included 130 images. (30 non-metastases and 100 metastases) collected from 60 volunteers. All images were obtained within approximately 3 hours after injecting a small amount of radioactive material 99mTc-MDP into the patients and then carrying out scanning with a gamma camera. The 10-fold cross-validation technique was used for all tests. RESULTS: CADBOSS could correctly identify in 120 out of 130 images. Thus, the accuracy, sensitivity, and specificity of CADBOSS were 92.30%, 94%, and 86.67%, respectively. Moreover, CADBOSS increased physician's success in detecting metastases from 95.38% to 96.9%. CONCLUSIONS: Detailed experiments showed that CADBOSS outperforms state-of-the-art computer-aided diagnosis. (CAD) systems and reasonably improves physician' diagnostic success.