3D Slicer 中的 SAM & SAM 2：用于标注医学图像的 SegmentWithSAM 扩展

Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification

Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data by experts. To fully exploit the easily available unlabeled data, we propose a novel Spatio-Temporal Structure Consistent (STSC) learning framework. Specifically, a gram matrix is derived to combine the spatial structure consistency and temporal structure consistency together. This gram matrix captures the structural similarity among the representations of different training sa…

TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning

Processing of medical images such as MRI or CT presents unique challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment of volumes. We present TorchIO, an open-source Python library to enable effi…

Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification

Cell detection is an essential task in cell image analysis. Recent deep learning-based detection methods have achieved very promising results. In general, these methods require exhaustively annotating the cells in an entire image. If some of the cells are not annotated (imperfect annotation), the detection performance significantly degrades due to noisy labels. This often occurs in real collaborations with biologists and even in public data-sets. Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data. A detection convolutional neural network (CNN)…

A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision

The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To alleviate this problem, this work proposes a new method to efficiently segment medical imaging volumes or videos using point-wise annotations only. This allows annotations to be collected extremely quickly and remains applicable to numerous segmentation tasks. Our approach trains a deep learning model using an appropriate Positive/Unlabeled objectiv…

BoxCell：利用SAM进行基于框监督的细胞分割

BoxCell: Leveraging SAM for Cell Segmentation with Box Supervision

提出BoxCell框架，利用SAM在训练和测试时将边界框解释为提示，通过整数规划调和两个分割掩码，在弱监督设置下显著提升细胞分割性能。

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious, laborious, and time consuming, but it also demands costly, specialty-oriented skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a si…