DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License

Registration and quantitative comparison of temporal mammograms (with application to HRT data)

In order to reduce the high mortality rate of breast cancer, most research in mammogram image analysis aims to develop a CAD (Computer Assisted Detection) system that can assist the clinician in the difficult task of "early" diagnosis. The comparison of temporal pairs of mammograms is believed by radiologists to be often crucial for diagnosing cancer, especially as breast tissue is highly variable across the population making it difficult to perform diagnosis reliably from a single mammogram. However, changes in the appearance of mammograms due to differences in compression and imaging conditions can limit the effectiveness of temporal comparison. In this thesis, a registration method that aligns temporal (or bilateral) mammograms is presented. This technique has the potential to assist the clinician to detect changes more efficiently (e.g. interval cancers in mammogram sequences, architectural distortions or microcalcifications in bilateral mammograms). In addition, the application of this technique to mammograms of Hormone Replacement Therapy (HRT) users is investigated. Since long-term use of HRT can increase the risk of breast cancer (as a side effect of glandular tissue regeneration), quantification of temporal tissue density changes (in addition to registration) is needed for assessing density changes locally. In order to derive quantitative measures of breast tissue change, the mammogram pairs are processed using the hint representation of interesting tissue. Since such a measure depends on the image information context, we examine the problems that arise from combining image registration and quantification, aiming to develop a robust framework for temporal tissue density change assessment

A map of the simulated tumor behavior regarding the dominant phenotypes as a function of the proliferation and diffusion rate of the invasive phenotype relative to phenotype 1 for A, B) the hypoxia-induced invasion (phenotype 2*) and C, D) the unconditional more invasive phenotype (phenotype 3*), under poor and well vascularized conditions, respectively.

<p>Points with blue color correspond to parametric pairs where the proliferative phenotype 1 dominates. With red color is represented the dominance of the invasive phenotype and with green color is depicted the region where a transition from the dominance of the proliferative phenotype 1 to the dominance of the invasive phenotype is observed.</p

Model parameters in dimensional and non-dimensional forms.

<p>Model parameters in dimensional and non-dimensional forms.</p

An <i>in-slico</i> tumor consisting of a proliferative (phenotype 1) and a hypoxia-induced invasive (phenotype 2) sub-population as grows under poorly-vascularized (on the left column) and well-vascularized conditions (on the right column).

<p>A, D) The evolution of the corresponding normoxic sub-populations of each phenotype illustrates the dominance of phenotype 2. B, E) A central cross section of the tumor at day 50 and day 200 showing the spatial distribution of the viable sub-populations of the two phenotypes, respectively. C, F) The spatial distribution of viable (normoxic and hypoxic) cells after 200 fictitious days varying from blue color at the lowest cell density to red color at the highest observed as depicted in the corresponding colorbar.</p

Values of characteristic properties for each tumor cell phenotype.

<p>Values of characteristic properties for each tumor cell phenotype.</p

Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip

Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep learning methodology with the use of transfer learning and a convolutional neural network (CNN) ensemble, for the accurate differentiation between the two diseases. An augmented dataset of 210 hips with TOH and 210 hips with AVN was used to finetune three ImageNet-trained CNNs (VGG-16, InceptionResNetV2, and InceptionV3). An ensemble decision was reached in a hard-voting manner by selecting the outcome voted by at least two of the CNNs. Inception-ResNet-V2 achieved the highest AUC (97.62%) similar to the model ensemble, followed by InceptionV3 (AUC of 96.82%) and VGG-16 (AUC 96.03%). Precision for the diagnosis of AVN and recall for the detection of TOH were higher in the model ensemble compared to Inception-ResNet-V2. Ensemble performance was significantly higher than that of an MSK radiologist and a fellow (P < 0.001). Deep learning was highly successful in distinguishing TOH from AVN, with a potential to aid treatment decisions and lead to the avoidance of unnecessary surgery