MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks

In gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-defined segmentation mask generated for one of the sequences. Our contributions lie in our proposed workflow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future. Best Paper Honorable Mention at VCBM2022publishedVersio

Generative adversarial networks for automated hippocampus segmentation : development of an artificial neural network and integration in a generative adversarial network scheme to improve the segmentation of the hippocampus and its substructures

Alzheimer’s Disease affects millions of people worldwide, but till today, the gold standard for definitive diagnosis of this disease is a biopsy. Nevertheless, with the progress of the disease, a volume loss in the Hippocampus can be observed. Therefore, good segmentation methods are crucial to facilitate quantification of this loss. The focus of this work is on the development of a Machine Learning algorithm, more precisely a Generative Adversarial Network, for the automated segmentation of the human Hippocampus and its substructures in Magnetic Resonance Images. In particular, the task is to determine if the integration of a pre-trained network that generates segmentations into a Generative Adversarial Network scheme can improve generated segmentations. In this context, a segmentation network in form of a U-net corresponds to the generator. The discriminator is developed separately and merged in a second step with the generator for combined training. With a literature review regarding the automated segmentation of the Hippocampus, current methods in this field and their medical and technological basics were identified. The datasets were preprocessed to make them suitable for the use in a neural network. In the training process, the generator was trained first until convergence. Then, the Generative Adversarial Network including the pre-trained generator was trained. The outcomes were evaluated via cross-validation in two different datasets (Kulaga-Yoskovitz and Winterburn). The Generative Adversarial Network scheme was tested regarding different architectural and training aspects, including the usage of skip-connections and a combined loss function. The best results were achieved in the Kulaga-Yoskovitz dataset with a Dice coefficient of 90.84 % after the combined training of generator and discriminator with a joined loss function. This improves the current state of the art method in the same task and dataset with a Dice index of 88.79 % by Romero [Rom17]. Except of two cases in the Winterburn dataset, the proposed combined method could always improve the Dice results after the training of only the generator, even though only by a small amount

Development and proof-of-concept of a multicenter, patient-centered cancer registry for breast cancer patients with metastatic disease — the “Breast cancer care for patients with metastatic disease” (BRE-4-MED) registry

Background: Patients with metastatic breast cancer (MBC) are treated with a palliative approach with focus oncontrolling for disease symptoms and maintaining high quality of life. Information on individual needs of patients andtheir relatives as well as on treatment patterns in clinical routine care for this specific patient group are lacking or arenot routinely documented in established Cancer Registries. Thus, we developed a registry concept specifically adaptedfor these incurable patients comprising primary and secondary data as well as mobile-health (m-health) data. Methods: The concept for patient-centered “Breast cancer care for patients with metastatic disease”(BRE-4-MED)registry was developed and piloted exemplarily in the region of Main-Franconia, a mainly rural region in Germanycomprising about 1.3 M inhabitants. The registry concept includes data on diagnosis, therapy, progression, patient-reported outcome measures (PROMs), and needs of family members from several sources of information includingroutine data from established Cancer Registries in different federal states, treating physicians in hospital as well as inoutpatient settings, patients with metastatic breast cancer and their family members. Linkage with routine cancerregistry data was performed to collect secondary data on diagnosis, therapy, and progression. Paper and online-basedquestionnaires were used to assess PROMs. A dedicated mobile application software (APP) was developed to monitorneeds, progression, and therapy change of individual patients. Patient’s acceptance and feasibility of data collection inclinical routine was assessed within a proof-of-concept study. Results: The concept for the BRE-4-MED registry was developed and piloted between September 2017 and May 2018.In total n= 31 patients were included in the pilot study, n= 22 patients were followed up after 1 month. Recordlinkage with the Cancer Registries of Bavaria and Baden-Württemberg demonstrated to be feasible. The voluntary APP/online questionnaire was used by n= 7 participants. The feasibility of the registry concept in clinical routine waspositively evaluated by the participating hospitals. Conclusion: The concept of the BRE-4-MED registry provides evidence that combinatorial evaluation of PROMs, needsof family members, and raising clinical parameters from primary and secondary data sources as well as m-healthapplications are feasible and accepted in an incurable cancer collective

Direct crosstalk between mast cell–TNF and TNFR1-expressing endothelia mediates local tissue inflammation

Signaling through tumor necrosis factor receptor 1 (TNFR1) controls bacterial infections and the induction of inflammatory Th1 cell–mediated autoimmune diseases. By dissecting Th1 cell–mediated delayed-type hypersensitivity responses (DTHRs) into single steps, we localized a central defect to the missing TNFR1 expression by endothelial cells (ECs). Adoptive transfer and mast cell knockin experiments into KitW/KitW-v, TNF−/−, and TNFR1−/− mice showed that the signaling defect exclusively affects mast cell–EC interactions but not T cells or antigen-presenting cells. As a consequence, TNFR1−/− mice had strongly reduced mRNA and protein expression of P-selectin, E-selectin, ICAM-1, and VCAM-1 during DTHR elicitation. In consequence, intravital fluorescence microscopy revealed up to 80% reduction of leukocyte rolling and firm adhesion in TNFR1−/− mice. As substitution of TNF−/− mice with TNF-producing mast cells fully restored DTHR in these mice, signaling of mast cell-derived TNF through TNFR1-expressing ECs is essential for the recruitment of leukocytes into sites of inflammation