DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-Attenuated Inversion Recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture; namely DeepSeg for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full resolution probability map. Based on modified U-Net architecture, different CNN models such as Residual Neural Network (ResNet), Dense Convolutional Network (DenseNet), and NASNet have been utilized in this study. Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of Brain Tumor Segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly. Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open-source and freely available at https://github.com/razeineldin/DeepSeg/.Comment: Accepted to International Journal of Computer Assisted Radiology and Surger

Supportive Care Needs in Glioma Patients and Their Caregivers in Clinical Practice: Results of a Multicenter Cross-Sectional Study

Objective: Supportive care needs in glioma patients often remain unrecognized, and optimization in assessment is required. First, we aimed at assessing the support needed using a simple structured questionnaire. Second, we investigated the psychosocial burden and support requested from caregivers.Methods: Patients were assessed at three centers during their outpatient visits. They completed the Distress Thermometer (DT; score ≥ 6 indicated significant burden in brain tumor patients), the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)-C30+BN20, and the Patients' Perspective Questionnaire (PPQ) that assessed psychosocial distress as well as support requested and received by patients for specific domains (e.g., family, doctor, and mobile care). In each subgroup, patients' caregivers were assessed simultaneously by a questionnaire developed for the study. Multivariate backward logistic regressions were performed for investigating predictors of patients' request for support.Results: Assessments were conducted for 232 patients. Most patients (82%) had a high-grade glioma and a mean age of 52 years (range 20–87). The male to female ratio was 1.25:1. According to the PPQ results, 38% (87) of the patients felt depressed; 44% (103), anxious; and 39% (91), tense/nervous. Desired support was highest from doctors (59%) and psychologists (19%). A general request for support was associated with lower global health status (p = 0.03, odds ratio (OR) = 0.96, 95% CI: 0.92–0.99) according to EORTC QLQ-C30. Most of the assessed caregivers (n = 96) were life partners (64%; n = 61) who experienced higher distress than the corresponding patients (caregivers: 6.5 ± 2.5 vs. patients: 5.3 ± 2.4). When patients were on chemotherapy, caregivers indicated DT ≥ 6 significantly more frequently than patients themselves (p = 0.02).Conclusion: Our data showed that glioma patients and their caregivers were both highly burdened. The PPQ allowed us to evaluate the psychosocial support requested and perceived by patients, detect supportive care needs, and provide information at a glance. Patients in poorer clinical condition are at risk of having unmet needs. The caregivers' burden and unmet needs are not congruent with the patients' need for support. In particular, caregivers of patients on chemotherapy were more highly burdened than patients themselves

Evaluation of the PCI-Simulator CathI

Die Ausbildung im Herzkatheterlabor hat sich seit Etablierung der interventionellen Kardiologie kaum verändert. Wie auch in anderen Bereichen der Medizin steht schon seit einiger Zeit die Frage nach der ethischen Vertretbarkeit des „see one, do one, teach one“ Prinzips im Raum. Das Dilemma zwischen der Sicherheit des Patienten und einer adäquaten Ausbildung der Assistenten führte schon früh zum Ruf nach Simulationssystemen für die Medizin. Heute existieren Geräte, die die technischen Voraussetzungen erfüllen, um die komplizierte menschliche Physiologie und Pathophysiologie zu simulieren. Eine gründliche wissenschaftliche Evaluation dieser Systeme ist die notwendige Basis, um die Ausbildung in der Medizin zu verbessern. Zur Validation des PCI-Simulators CathI wurde an der Medizinischen Klinik der Universität Würzburg eine formative und summative Evaluation durchgeführt. Als erster Schritt wurden die Lerneffekte zweier randomisierter Laiengruppen, einer am CathI-System (n=7) und einer an einem Computerprogramm trainierten Gruppe(n=6) während eines viertägigen Trainings untersucht. Nach Ablauf des Trainings wurden die Ergebnisse zur Überprüfung der konkurrenten Validität und der Eignung des Systems als Assessment-Tool, mit einer Expertengruppe verglichen. Als letzten Schritt testete man die am CathI-System erworbenen interventionellen Fähigkeiten im Herzkatheterlabor. Hierbei wurde eine am CathI-System trainierte (n=6), mit einer computerbasiert trainierten Gruppe (n=6) während einer Intervention an einem pulsatilen röntgenfähigen Herzmodell verglichen. Die Evaluation erfolgte an Hand objektiver Standardparameter und mittels eines Fähigkeiten-Scores. Als eines der wichtigsten Ergebnisse konnte gezeigt werden, dass Probanden durch Training am CathI-Simulator besser auf kardiologische Grundfähigkeiten vorbereitet werden als eine vergleichbare Gruppe durch Ausbildung an einer Computersimulation. Das CathI-System führt zu einem risikoärmeren Verhalten im Herzkatheterlabor. Man kann daraus folgern, dass das CathI-System eine überlegene Ausbildungsmöglichkeit für die Vorbereitung auf eine Intervention am Patienten darstellt. Der CathI-Simulator ermöglicht es Experten, basierend auf ihren Fähigkeiten in der interventionellen Kardiologie auch die Simulation mit hoher Qualität zu absolvieren. Es kann konkurrente Validität für das Trainingsgerät angenommen werden. Dem Simulationssystem CathI ist es möglich, die Expertise des Anwenders realitätsnah einzuschätzen. Es kann spezifisch kognitive und motorische Fähigkeiten testen. Das Simulationssystem CathI erwies sich als effektives Werkzeug zur Schulung der diagnostischen Koronarangiographie und der PCI.Background: The virtual performance of carotid stenting became an integral part of the resident training, which was just recently recognised by the Federal Drug Administration in the United States. It just a matter of time until this requirements are extended to the coronary angiography and the PTCA. Even more important is a sufficiently validated simulation system as a base for excellent and continuous education. The purpose of this study therefore was to evaluate the construct and concurrent validity of the CathI PCI Simulator. Methods: For evaluating the concurrent validity a novice group of 7 volunteering final year medical students was trained for 4 consecutive days performing three standardised procedures each day. On day 4 the performance of the novices was compared with an experts group, consisting of 6 experienced interventionists. The construct validity was evaluated by assessing the intervention of two novice groups of 5 participants each on a pulsating heard model. Each of them received a similar training schedule of 4 days. One Group was trained on the CathI-System (CATHI) the other performed the same tasks using a standard computer interface (PC). Their performance was evaluated using a skill-score as well as by assessing objective parameters like interventional or fluoroscopy time. Results: The experts showed significant superiority for the overall procedure time with the highest difference for the diagnostic time. Significantly less contrast agent was used by the experts, while there was no difference for the usage of fluoroscopy and the frequency of “perforating” the end of a vessel. The comparison of the two novice groups in the cardiac suite showed a significant higher score of the CATHI group. With regard to the single components of the score there was significantly handling skill and a better risk awareness of the CATHI group. The assessed objective parameters showed no significant difference. Conclusions: We could show concurrent and construct validity of the CathI Simulator. Traing with the CathI-Simulator teaches the basic skills for the PCI and leads to a risk reduction in the cardiac-suite. It is an adequate tool to teach the PCI and to assess the level of experience of its user

Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Intraoperative brain deformation, so called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery

The Impact of an Ultra-Early Postoperative MRI on Treatment of Lower Grade Glioma

The timing of MRI imaging after surgical resection may have an important role in assessing the extent of resection (EoR) and in determining further treatment. The aim of our study was to evaluate the time dependency of T2 and FLAIR changes after surgery for LGG. The Log-Glio database of patients treated at our hospital from 2016 to 2021 was searched for patients >18a and non-enhancing intra-axial lesion with complete MR-imaging protocol. A total of 16 patients matched the inclusion criteria and were thus selected for volumetric analysis. All patients received an intraoperative scan (iMRI) after complete tumor removal, an ultra-early postoperative scan after skin closure, an early MRI within 48 h and a late follow up MRI after 3–4 mo. Detailed volumetric analysis of FLAIR and T2 abnormalities was conducted. Demographic data and basic characteristics were also analyzed. An ultra-early postoperative MRI was performed within a median time of 30 min after skin closure and showed significantly lower FLAIR (p = 0.003) and T2 (p = 0.003) abnormalities when compared to early postoperative MRI (median 23.5 h), though no significant difference was found between ultra-early and late postoperative FLAIR (p = 0.422) and T2 (p = 0.575) images. A significant difference was calculated between early and late postoperative FLAIR (p = 0.005) and T2 (p = 0.019) MRI scans. Additionally, we found no significant difference between intraoperative and ultra-early FLAIR/T2 (p = 0.919 and 0.499), but we found a significant difference between iMRI and early MRI FLAIR/T2 (p = 0.027 and p = 0.035). Therefore, a postoperative MRI performed 24 h or 48 h might lead to false positive findings. An MRI scan in the first hour after surgery (ultra-early) correlated best with residual tumor at 3 months follow up. An iMRI with open skull, at the end of resection, was similar to an ultra-early MRI with regard to residual tumor

Kaplan Meier Plot of progression free survival by iMRI and 5-ALA&iMRI assisted surgery.

<p>iMRI intraoperative MRI; 5-ALA: 5 Aminolevulinic acid.</p

Insertion of Ruthenium into an inorganic, cyclic biradicaloid

The reaction between [$Ru_2Cl_2(μ_2-Cl)_2(CO)_6]$ and the biradicaloid [$P(μ-NTer)]_2$ proceeds under insertion of a $Ru(II)Cl(CO)_2$ fragment into one $P-N$ bond and addition of chloride to the adjacent phosphorus center. Thereby an unprecedented inorganic ruthenacycle is obtained that was investigated by single crystal X-ray analysis, $NMR$ and $IR$ pectroscopy and $DFT$ calculations. The reactivity of the complex in a halide-pseudo-halide exchange reaction and a coordination reaction were investigated.ISSN:1521-3749ISSN:0044-231

distribution of matching criteria.

<p>iMRI: intraoperative high field MRI; 5-ALA: 5-aminolevulinic acid; cc: cubic centimeter</p><p>distribution of matching criteria.</p

Explainability of deep neural networks for MRI analysis of brain tumors

Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI

Volumetric assessment.

<p>iMRI: intraoperative high-field MRI; 5-ALA: 5-aminolevulinic acid; EoR: extent of resection</p><p>Volumetric assessment.</p