GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the first version: padding, loss, typos and update of the DOI numbe

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes

Data-driven synthesis of composite-feature detectors for 3D image analysis

Most image analysis techniques are based upon low level descriptions of the data. It is important that the chosen representation is able to discriminate as much as possible among independent image features. In particular, this is of great importance in segmentation with deformable models, which must be guided to the target object boundary avoiding other image features. In this paper, we present a multiresolution method for the decomposition of a volumetric image into its most relevant visual patterns, which we define as features associated to local energy maxima of the image. The method involves the clustering of a set of predefined band-pass energy filters according to their ability to segregate the different features in the image. In this way, the method generates a set of composite-feature detectors tuned to the specific visual patterns present in the data. Clustering is accomplished by defining a distance metric between the frequency features that reflects the degree of alignment of their energy maxima. This distance is related to the mutual information of their responses' energy maps. As will be shown, the method is able to isolate the frequency components of independent visual patterns in 3D images. We have applied this composite-feature detection method to the initialization of active models. Among the visual patterns detected, those associated to the segmentation target are selected by user interaction to define the initial state of a geodesic active model. We will demonstrate that this initialization technique facilitates the evolution of the model to the proper boundary.S

Computer-Aided Diagnosis in Neuroimaging

This chapter is intended to provide an overview to the most used methods for computer-aided diagnosis in neuroimaging and its application to neurodegenerative diseases. The fundamental preprocessing steps, and how they are applied to different image modalities, will be thoroughly presented. We introduce a number of widely used neuroimaging analysis algorithms, together with a wide overview on the recent advances in brain imaging processing. Finally, we provide a general conclusion on the state of the art in brain imaging processing and possible future developments