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Anonymous Supplement for the research paper CONTEXTLOSS: CONTEXT INFORMATION FOR TOPOLOGY-PRESERVING SEGMENTATION.

1 Anonymous Supplement for the research paper CONTEXTLOSS: CONTEXT INFORMATION FOR TOPOLOGY-PRESERVING SEGMENTATION

Supplement for the research paper CONTEXTLOSS: CONTEXT INFORMATION FOR TOPOLOGY-PRESERVING SEGMENTATION

Low-cost and high-precision infrared meibomian gland segmentation is an important basis for early diagnosis and monitoring of many ocular diseases in ophthalmic clinical practice. To address the issue of limited labeled data, we propose a novel semi-supervised meibomian gland segmentation approach. By leveraging the prior knowledge of the patient each image belongs to, intra-patient registration is taken to generate diverse and lifelike pseudo-labeled data.

Needle localization in ultrasound images is pivotal for the successful execution of ultrasound-guided core needle biopsies. Automating the needle detection process can decrease the procedure time and lead to a more precise diagnosis. In this article, we introduce an automatic method for detecting the core needle and determining its trajectory in 2D ultrasound images.

This paper introduces a novel optimization approach for stain separation in digital histopathological images. Our stain separation cost function incorporates a smooth total variation regularization and is minimized by using a projected gradient algorithm. To enhance computational efficiency and enable supervised learning of the hyperparameters, we further unroll our algorithm into a neural network. The unrolled architecture is not only more efficient for solving the stain separation problem, but also allows to design a highly interpretable and flexible method.

Vision Transformer (ViT) has become a state-of-art in many vision tasks, owing to its great scalability and promising performance. In Magnetic Resonance Imaging (MRI), motion continues to be a major problem, which degrades the image quality and the corresponding disease assessment. The purpose of this work is to assess a ViT-based MRI motion correction method. Self-supervised learning was further incorporated to enhance the motion correction effects.

Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computing capabil- ities to support computer-aided diagnosis. Many methods employ factorized convolutional layers to alleviate the bur- den of limited computational resources at the expense of expressiveness.

Bone marrow smear cytology involves observing and analyzing the morphological features of bone marrow cells, and identifying regions of interest (ROI) where the cells are morphologically clear and evenly distributed is a crucial part of this process. However, existing deep learning methods for selecting ROI in whole-slide images (WSI) of bone marrow smears have overlooked the unique characteristics of the smears, particularly the texture information, resulting in inadequate performance.

Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficient neural network for retinal vessel segmentation in OCTA images. The proposed network achieves accuracy comparable to other SOTA methods, while having fewer parameters and faster inference speed (e.g. 110x lighter and 1.3x faster than U-Net), which is very friendly for industrial applications. This is achieved by applying the modified Recurrent ConvNeXt Block to a full resolution convolutional network.