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Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.

Automatic segmentation of the intracranial artery (IA) in digital subtraction angiography (DSA) sequence is an essential step in diagnosing IA-related diseases and guiding neuro-interventional surgery. However, the lack of publicly available datasets has impeded research in this area. In this paper, we release DIAS, an IA segmentation dataset, consisting of 120 DSA sequences from intracranial interventional therapy. In addition to pixel-wise annotations, this dataset provides two types of scribble annotations for weakly supervised IA segmentation research. We present a comprehensive benchmark for evaluating the performance of this challenging dataset by utilizing fully-, weakly-, and semi-supervised learning approaches. Specifically, we propose a method that incorporates a dimensionality reduction module into a 2D/3D model to achieve vessel segmentation in DSA sequences. For weakly-supervised learning, we propose a scribble learning-based image segmentation framework, SSCR, which comprises scribble supervision and consistency regularization. Furthermore, we introduce a random patch-based self-training framework that utilizes unlabeled DSA sequences to improve segmentation performance. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications.

Imaging markers of cerebral small vessel disease provide valuable information on brain health,but their manual assessment is time-consuming and hampered by substantial intra- and interratervariability. Automated rating may benet biomedical research, as well as clinical assessment, butdiagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscularLesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite eventat the international conference on Medical Image Computing and Computer Aided Intervention(MICCAI) 2021. This challenge aimed to promote the development of methods for automateddetection and segmentation of small and sparse imaging markers of cerebral small vessel disease,namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunesof presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teamsparticipated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 forTask 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training andevaluation. Results showed a large variability in performance both across teams and across tasks,with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practicallyuseful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency acrosscases that may deter use at an individual level, while still proving useful at a population level.Keywords: CSVD, brain, MRI, microbleeds, enlarged perivascular spaces, lacunes, automated,segmentation, detection, challenge