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The last few years have seen a boom in using generative models to augment real datasets, as synthetic data can effectively model real data distributions and provide privacy-preserving, shareable datasets that can be used to train deep learning models. However, most of these methods are 2D and provide synthetic datasets that come, at most, with categorical annotations. The generation of paired images and segmentation samples that can be used in downstream, supervised segmentation tasks remains fairly uncharted territory. This work proposes a two-stage generative model capable of producing 2D and 3D semantic label maps and corresponding multi-modal images. We use a latent diffusion model for label synthesis and a VAE-GAN for semantic image synthesis. Synthetic datasets provided by this model are shown to work in a wide variety of segmentation tasks, supporting small, real datasets or fully replacing them while maintaining good performance. We also demonstrate its ability to improve downstream performance on out-of-distribution data., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Virginia Fernandez reports financial support, administrative support, and travel were provided by EPSRC Centre for Doctoral Training in Smart Medical Imaging. Tom Vercauteren reports a relationship with Hypervision Surgical that includes: board membership and equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mark Graham, Paul Wright, Walter Diaz Sanz, Parashkev Nachev, Sebastian Ourselin, Geraint Rees reports financial support was provided by Wellcome Trust. Yee Mah reports financial support was provided by UKRI Medical Research Council., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27-altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Primary central nervous system lymphoma (PCNSL) is an aggressive disease with previously poor prognosis. The advent of high-dose methotrexate-based induction regimens as well as use of consolidation therapy has greatly improved this prognosis in recent decades, but durable remission still eludes half of patients. In this review, we summarize the progress made in the treatment of PCNSL as well as the challenges that remain, with a focus on defining optimal induction and consolidation regimens, including the promise of developing biotherapies. Future studies will help delineate the best combination of existing and novel treatment strategies, with the goal of expanding the cohort of patients achieving a cure., (Copyright © 2018. Published by Elsevier Ltd.)