Automatic CT Angiography Lesion Segmentation Compared to CT Perfusion in Ischemic Stroke Detection: a Feasibility Study

In stroke imaging, CT angiography (CTA) is used for detecting arterial occlusions. These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)-based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. These results were compared to volumes reported by widely used CT perfusion-based RAPID software (IschemaView). A 42-layer-deep CNN was trained on 50 CTA volumes with manually delineated targets. The lower bound for predicted lesion size to reliably discern stroke from false positives was estimated. The severity of false positives and false negatives was reviewed visually to assess the clinical applicability and to further guide the method development. The CNN model corresponded to the manual segmentations with voxel-wise sensitivity 0.54 (95% confidence interval: 0.44-0.63), precision 0.69 (0.60-0.76), and Sorensen-Dice coefficient 0.61 (0.52-0.67). Stroke/nonstroke differentiation accuracy 0.88 (0.81-0.94) was achieved when only considering the predicted lesion size (i.e., regardless of location). By visual estimation, 46% of cases showed some false findings, such as CNN highlighting chronic periventricular white matter changes or beam hardening artifacts, but only in 9% the errors were severe, translating to 0.91 accuracy. The CNN model had a moderately strong correlation to RAPID-reported T-max > 10 s volumes (Pearson's r = 0.76 (0.58-0.86)). The results suggest that detecting anterior circulation ischemic strokes from CTA using a CNN-based algorithm can be feasible when accompanied with physiological knowledge to rule out false positives.Peer reviewe

Marker-controlled Watershed with Deep Edge Emphasis and Optimized H-minima Transform for Automatic Segmentation of Densely Cultivated 3D Cell Nuclei

Background The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively. Results The dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches. Conclusions The use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.Peer reviewe

Automaattisten kuvankäsittelymenetelmien integrointi kliiniseen rutiiniin

Medical imaging examinations are an essential part of modern medicine. The acquired images are used for diagnostics, treatment planning, and to follow-up the progression of diseases. However, some of the acquired image series require post-processing and analyzing before the primary result is achieved from the images. For example, quantitative imaging requires image analysis. Manual medical image analysis can be a time consuming task. Image series are large and the operator have to execute all tasks in the analysis workflow in addition to the execution of the analysis application. For example, these tasks can include image transmissions and file format conversions. Also, the result of the multistep analysis process can vary depending on the operator. The purpose of this thesis is to implement an automated medical image analysis workflow management system for the needs of HUS (The Hospital District of Helsinki and Uusimaa) Medical Imaging Center. The workflow management system is implemented with Python and different open source software libraries, such as DICOM Toolkit, pydicom and SimpleITK. The implemented system receives the images from the image archive and recognizes the required analysis task for an image series by reading the metadata of the series. The workflow management system starts the analysis execution, converts the results into a file format which is compatible with the image archive of the hospital and transfers the results into the image archive.Lääketieteelliset kuvantamistutkimukset ovat tärkeä osa nykyaikaista lääketiedettä. Tutkimuksista saatuja kuvia käytetään lääketieteessä sairauksien diagnosoimiseen, hoidon suunnitteluun sekä sairauksien etenemisen seurantaan. Osa kuvantamistut- kimuksista saaduista kuvasarjoista vaatii kuvankäsittelyä, jotta kuvista saataisiin haluttu informaatio. Esimerkiksi kvantitatiiviset tutkimukset vaativat kuvien analy- sointia. Manuaalinen kuvankäsittely voi olla aikaa vievä tehtävä. Kuvasarjat ovat suuria ja suoritettavan analyysiohjelman lisäksi käyttäjän pitää suorittaa myös muita analyysimenetelmän työnkulun vaiheita. Näitä vaiheita ovat esimerkiksi tiedostojen siirtäminen sairaalan kuva-arkistosta työpöydälle ja tiedostoformaatin muutokset. Monivaiheisen analyysiprosessin lopputulokset voivat myös vaihdella käyttäjästä riippuen. Tämän työn tarkoituksena on kehittää automatisoitu kuvankäsittelyn työnohjaus- järjestelmä HUS (Helsingin ja Uudenmaan sairaanhoitopiiri) Kuvantamisen tarpeisiin. Järjestelmä on toteutettu Pythonilla sekä hyödyntäen avoimen lähdekoodin ohjelmistokirjastoja, kuten DICOM Toolkit, pydicom ja SimpleITK. Kehitetty järjestelmä vastaanottaa kuva-arkistosta lähetetyt kuvat ja tunnistaa kuvasarjalle suoritettavan käsittelyoperaation lukemalla kuvan metatietoja. Järjestelmä suorittaa valitut analyysit ja muuttaa analyysin lopputuloksen sairaalan kuva-arkistoon sopivaksi tiedostoformaatiksi ja lähettää tulokset sairaalan kuva-arkistoon