Quantifying the calcification of abdominal aorta and major side branches with deep learning.

AIM: To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches. MATERIALS AND METHODS: In total, 58 computed tomography (CT) angiography volumes were selected from a dataset of 609 to represent different stages of sclerosis. The ground truth segmentations of the abdominal aorta, coeliac trunk, superior mesenteric artery, renal arteries, common iliac arteries, and their calcifications were delineated manually. Two V-Net ensemble models were trained, one for segmenting arteries of interest and another for calcifications. The branches of interest were shortened algorithmically. The volumes of calcification were then evaluated from the arteries of interest. RESULTS: The results indicate that automatic detection is possible with a high correlation to the ground truth. The scores for the ensemble calcification model were dice score of 0.69 and volumetric similarity (VS) of 0.80 and for the arteries of interest segmentations: aorta: dice 0.96, VS 0.98; aortic branches: dice 0.74, VS 0.87; and common iliac arteries: dice 0.72, VS 0.91. CONCLUSIONS: The presented neural network model is the first to be capable of automatically segmenting, in addition to calcification, both the aorta and its branches from contrast-enhanced CT angiography. This technology shows promise in addressing limitations inherent in earlier methods that relied solely on plain CT.

Automatic detection of Crohn's disease using quantified motility in magnetic resonance enterography: initial experiences.

AIM: To report initial experiences of automatic detection of Crohn's disease (CD) using quantified motility in magnetic resonance enterography (MRE). MATERIALS AND METHODS: From 302 patients, three datasets with roughly equal proportions of CD and non-CD cases with various illnesses were drawn for testing and neural network training and validation. All datasets had unique MRE parameter configurations and were performed in free breathing. Nine neural networks were devised for automatic generation of three different regions of interests (ROI): small bowel, all bowel, and non-bowel. Additionally, a full-image ROI was tested. The motility in an MRE series was quantified via a registration procedure, which, accompanied with given ROIs, resulted in three motility indices (MI). A subset of the indices was used as an input for a binary logistic regression classifier, which predicted whether the MRE series represented CD. RESULTS: The highest mean area under the curve (AUC) score, 0.78, was reached using the full-image ROI and with the dataset with the highest cine series length. The best AUC scores for the other two datasets were only 0.54 and 0.49. CONCLUSION: The automatic system was able to detect CD in the group of MRE studies with lower temporal resolution and longer cine series showing potential in primary bowel disorder diagnostics. Larger ROI selections and utilising all available cine series for motility registration yielded slight performance improvements.

Combined use of non-invasive techniques for improved functional localization for a selected group of epilepsy surgery candidates.

Invasive cortical mapping is conventionally required for preoperative identification of epileptogenic and eloquent cortical regions before epilepsy surgery. The decision on the extent and exact location of the resection is always demanding and multimodal approach is desired for added certainty. The present study describes two non-invasive preoperative protocols, used in addition to the normal preoperative work-up for localization of the epileptogenic and sensorimotor cortical regions, in two young patients with epilepsy. Magnetoencephalography (MEG) was used to determine the primary somatosensory cortex (S1) and the ictal onset zones. Navigated transcranial magnetic stimulation (nTMS) was used to determine the location and the extent of the primary motor representation areas. The localization results from these non-invasive methods were used for guiding the subdural grid deployment and later compared with the results from electrical cortical stimulation (ECS) via subdural grids, and validated by surgery outcome. The results from MEG and nTMS localizations were consistent with the ECS results and provided improved spatial precision. Consistent results of our study suggest that these non-invasive methods can be added to the standard preoperative work-up and may even hold a potential to replace the ECS in a subgroup of patients with epilepsy who have the suspected epileptogenic zone near the sensorimotor cortex and seizures frequent enough for ictal MEG.

Registration and display of brain SPECT and MRI using external markers.

Accurate anatomical localisation of abnormalities observed in brain perfusion single-photon emission computed tomography (SPECT) is difficult, but can be improved by correlating data from SPECT and other tomographic imaging modalities. For this purpose we have developed software to register, analyse and display 99mTc-hexamethylpropyleneamine oxime SPECT and 1.0 T MRI of the brain. For registration of SPECT and MRI data external skin markers containing 99mTc (220 kBq) in 50 microliters of coconut butter were used. The software is coded in the C programming language, and the X Window system and the OSF/Motif standards are used for graphics and definition of the user interface. The registration algorithm follows a noniterative least-squares method using singular value decomposition of a 3 x 3 covariance matrix. After registration, the image slices of both data sets are shown at identical tomographic levels. The registration error in phantom studies was on average 4 mm. In the two-dimensional display mode the orthogonal cross-sections of the data sets are displayed side by side. In the three-dimensional mode MRI data are displayed as a surface-shaded 3 D reconstruction and SPECT data as cut planes. The usefulness of this method is demonstrated in patients with cerebral infarcts, brain tumour, herpes simplex encephalitis and epilepsy.