In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from small sets of training data, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r ≥ 0.97) also with the expected lesion volume.

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

The characteristic MR features of lipoma arborescens allows an accurate diagnosis of this rare lesion, which is almost always associated with other chronic pathology of the joint in the elderly.

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling.

BackgroundNociceptive stimuli may evoke brain responses longer than the stimulus duration often partially detected by conventional neuroimaging. Fibromyalgia patients typically complain of severe pain from gentle stimuli. We aimed to characterize brain response to painful pressure in fibromyalgia patients by generating activation maps adjusted for the duration of brain responses.Methodology/Principal FindingsTwenty-seven women (mean age: 47.8 years) were assessed with fMRI. The sample included nine fibromyalgia patients and nine healthy subjects who received 4 kg/cm2 of pressure on the thumb. Nine additional control subjects received 6.8 kg/cm2 to match the patients for the severity of perceived pain. Independent Component Analysis characterized the temporal dynamics of the actual brain response to pressure. Statistical parametric maps were estimated using the obtained time courses. Brain response to pressure (18 seconds) consistently exceeded the stimulus application (9 seconds) in somatosensory regions in all groups. fMRI maps following such temporal dynamics showed a complete pain network response (sensory-motor cortices, operculo-insula, cingulate cortex, and basal ganglia) to 4 kg/cm2 of pressure in fibromyalgia patients. In healthy subjects, response to this low intensity pressure involved mainly somatosensory cortices. When matched for perceived pain (6.8 kg/cm2), control subjects showed also comprehensive activation of pain-related regions, but fibromyalgia patients showed significantly larger activation in the anterior insula-basal ganglia complex and the cingulate cortex.Conclusions/SignificanceThe results suggest that data-driven fMRI assessments may complement conventional neuroimaging for characterizing pain responses and that enhancement of brain activation in fibromyalgia patients may be particularly relevant in emotion-related regions.

Hemangioma is a common benign vascular neoplasm that closely resembles normal vessels and can be found in all organs of the human body. Vascular lesions can be classified as infantile hemangiomas or vascular malformations on the basis of their natural history, location, cellular turnover, and histologic characteristics. The magnetic resonance (MR) imaging features of vascular malformations of the central nervous system depend on the pathologic subtype. Soft-tissue vascular malformations can be categorized with combined MR imaging and MR angiography as either high- or low-flow. Osseous vascular malformations commonly demonstrate a high-signal-intensity trabecular pattern at both T1- and T2-weighted MR imaging. A group of more aggressive vascular neoplasms, including hemangioendothelioma, hemangiopericytoma, and glomus tumor, have a nonspecific appearance at MR imaging. In the liver and spleen, hemangiomas are typically hyperintense at T2-weighted MR imaging, with a centripetal filling pattern after administration of gadopentetate dimeglumine. Vascular lesions can involve several organs or systems in angiomatous syndromes. MR imaging allows characterization of a hemangioma with typical features, which vary depending on anatomic location. Familiarity with these features facilitates diagnosis and management of these anomalies.

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented.

Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.