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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

In medical imaging, 3D convolutional neural networks (CNNs) have shown superior performance to 2D CNN in numerous deep learning tasks with high dimensional input, proving the added value of 3D spatial information in feature representation. However, 3D CNN requires more training samples to converge, and more computational resources and execution time for both training and inference. Meanwhile, applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D networks. To tackle with these issues, we propose a novel 2D strategical representation of volumetric data, namely 2.75D approach. In our method, the spatial information of 3D images was captured in a single 2D view by a spiral-spinning technique. Therefore, our CNN is intrinsically a 2D network, which can fully leverage pre-trained 2D CNNs for downstream vision problems. We evaluated the proposed method on LUNA16 nodule detection challenge, by comparing the proposed 2.75D method with 2D, 2.5D, 3D counterparts in the nodule false positive reduction. Results show that the proposed method outperforms other counterparts when all methods were trained from scratch. Such performance gain is more pronounced when introducing transfer learning or when training data is limited. In addition, our method achieves a substantial reduce in time consumption of training and inference comparing with the 3D method. Our code will be publicly available.