U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical image segmentation. It aims to augment the infusion of semantic information into low-level features while simultaneously refining high-level features with finer details. For an input image, we begin by extracting multi-level features with a deep neural network encoder. Next, we enhance the feature map of each level by infusing semantic information from higher-level features and integrating finer details from lower-level features through Hadamard product. Our novel skip connections empower features of all the levels with enriched semantic characteristics and intricate details. The improved features are subsequently transmitted to the decoder for further processing and segmentation. Our method can be seamlessly integrated into any Encoder-Decoder network. We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency. Code is available at: https://github.com/yaoppeng/U-Net_v

Geodesic Graph Cut Based Retinal Fluid Segmentation in Optical Coherence Tomography

Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. Its most damaging form is characterized by accumulation of fluid inside the retina, whose quantification is of utmost importance for evaluating the disease progression. In this paper we propose an automated method for retinal fluid segmentation from 3D images acquired with optical coherence tomography (OCT). It combines a machine learning approach with an effective segmentation framework based on geodesic graph cut. After an image preprocessing step, an artificial neural network is trained based on textural features to assign to each voxel a probability of belonging to a fluid. The obtained probability maps are used to compute minimal geodesic distances from a set of identified seed points to the remaining unassigned voxels. Finally, the segmentation is solved optimally and efficiently using graph cut optimization. The method is evaluated on a clinical longitudinal dataset consisting of 30 OCT scans from 10 patients taken at 3 different stages of treatment. Manual annotations from two retinal specialists were taken as the gold standard. The segmentation method achieved mean precision of 0.88 and recall of 0.83, with the combined F1 score of 0.85. The segmented fluid volumes were within the measured inter-observer variability. The results demonstrate that the proposed method is a promising step towards accurate quantification of retinal fluid

PHG-Net: Persistent Homology Guided Medical Image Classification

Modern deep neural networks have achieved great successes in medical image analysis. However, the features captured by convolutional neural networks (CNNs) or Transformers tend to be optimized for pixel intensities and neglect key anatomical structures such as connected components and loops. In this paper, we propose a persistent homology guided approach (PHG-Net) that explores topological features of objects for medical image classification. For an input image, we first compute its cubical persistence diagram and extract topological features into a vector representation using a small neural network (called the PH module). The extracted topological features are then incorporated into the feature map generated by CNN or Transformer for feature fusion. The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion. We evaluate our PHG-Net on three public datasets and demonstrate its considerable improvements on the target classification tasks over state-of-the-art methods.Comment: Accepted by WACV 202

Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images

This paper describes an algorithm for extracting pulmonary vascular trees (arteries plus veins) from three-dimensional (3D) thoracic computed tomographic (CT) images. The algorithm integrates tube enhancement filter and traversal approaches which are based on eigenvalues and eigenvectors of a Hessian matrix to extract thin peripheral segments as well as thick vessels close to the lung hilum. The resultant algorithm was applied to a simulation data set and 44 scans from 22 human subjects imaged via multidetector-row CT (MDCT) during breath holds at 85% and 20% of their vital capacity. A quantitative validation was performed with more than 1000 manually identified points selected from inside the vessel segments to assess true positives (TPs) and 1000 points randomly placed outside of the vessels to evaluate false positives (FPs) in each case. On average, for both the high and low volume lung images, 99% of the points was properly marked as vessel and 1% of the points were assessed as FPs. Our hybrid segmentation algorithm provides a highly reliable method of segmenting the combined pulmonary venous and arterial trees which in turn will serve as a critical starting point for further quantitative analysis tasks and aid in our overall goal of establishing a normative atlas of the human lung

Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration

Age related macular degeneration is a major cause of blindness and visual impairment in older adults. Its exudative form, where fluids leak into the macula, is especially damaging. The standard treatment involves injections of anti-VEGF (vascular endothelial growth factor) agents into the eye, which prevent further vascular growth and leakage, and can restore vision. These intravitreal injections have a risk of devastating complications including blindness from infection and are expensive. Optimizing the interval between injections in a patient specific manner is of great interest, as the retinal response is partially patient specific. In this paper we propose a machine learning approach to predict the retinal response at the end of a standardized 12-week induction phase of the treatment. From a longitudinal series of optical coherence tomography (OCT) images, a number of quantitative measurements are extracted, describing the underlying retinal structure and pathology and its response to initial treatment. After initial feature selection, the selected set of features is used to predict the treatment response status at the end of the induction phase using the support vector machine classifier. On a population of 30 patients, leave-one-out cross-validation showed the classification success rate of 87% of predicting whether the subject will show a response to the treatment at the next visit. The proposed methodology is a promising step towards the much needed image-guided prediction of patient-specific treatment response