Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop, MICCAI 201

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images

We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many visual categories and instances. Our approach is based on a parametric figureground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorithm that produces a complete scene estimate. Our contributions can be summarized as follows: (1) a generalization of parametric max flow figure-ground proposal methodology to take advantage of intensity and depth information, in order to systematically and efficiently generate the breakpoints of an underlying spatial model in polynomial time, (2) new region description methods based on second-order pooling over multiple features constructed using both intensity and depth channels, (3) a principled search-based structured prediction inference and learning process that resolves conflicts in overlapping spatial partitions and selects regions sequentially towards complete scene estimates, and (4) extensive evaluation of the impact of depth, as well as the effectiveness of a large number of descriptors, both pre-designed and automatically obtained using deep learning, in a difficult RGB-D semantic segmentation problem with 92 classes. We report state of the art results in the challenging NYU Depth Dataset V2 [44], extended for the RMRC 2013 and RMRC 2014 Indoor Segmentation Challenges, where currently the proposed model ranks first. Moreover, we show that by combining second-order and deep learning features, over 15% relative accuracy improvements can be additionally achieved. In a scene classification benchmark, our methodology further improves the state of the art by 24%

Inertial-aided Visual Perception of Geometry and Semantics

We describe components of a visual perception system to understand the geometry and semantics of the three-dimensional scene by utilizing monocular cameras and inertial measurement units (IMUs). The use of the two sensor modalities is motivated by the wide availability of the camera-IMU sensor packages present in mobile devices from phones to cars, and their complementary sensing capabilities: IMUs can track the motion of the sensor platform over a short period of time accurately, and provide a scaled and gravity-aligned global reference frame, while cameras can capture rich photometric signatures of the scene, and provide relative motion constraints between images up to scale. We first show that visual 3D reconstruction can be improved by leveraging the global orientation frame -- easily inferred from inertials. In the gravity-aligned global orientation frame, a shape prior can be imposed in depth prediction from a single image, where the normal vectors to surfaces of objects of certain classes tend to align with gravity or orthogonal to it. Adding such a prior to baseline methods for monocular depth prediction yields improvements beyond the state-of-the-art and illustrates the power of utilizing inertials in 3D reconstruction. The global reference provided by inertials is not only gravity-aligned but also scaled, which is exploited in depth completion: We describe a method to infer dense metric depth from camera motion and sparse depth as estimated using a visual-inertial odometry system. Unlike other scenarios using point clouds from lidar or structured light sensors, we have few hundreds to few thousand points, insufficient to inform the topology of the scene. Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points. We use a predictive cross-modal criterion, akin to “self-supervision,” measuring photometric consistency across time, forward-backward pose consistency, and geometric compatibility with the sparse point cloud. We also launch the first visual-inertial + depth dataset (dubbed ``VOID''), which we hope will foster additional exploration into combining the complementary strengths of visual and inertial sensors. To compare our method to prior work, we adopt the unsupervised KITTI depth completion benchmark, and show state-of-the-art performance on it.In addition to dense geometry, the camera-IMU sensor package can also be used to recover the semantics of the scene. We present two methods to augment a point cloud map with class-labeled objects represented in the form of either scaled and oriented bounding boxes or CAD models. The tradeoff of the two shape representation resides in their generality and capability to model detailed structures. While being more generic, 3D bounding boxes fail to model the details of the objects, whereas CAD models preserve the finest shape details but require more computation and are limited to previously seen objects. Nevertheless, both methods populate an unknown environment with 3D objects placed in a Euclidean reference frame inferred causally and on-line using monocular video along with inertial sensors. Besides, both methods include bottom-up and top-down components, whereby deep networks trained for detection provide likelihood scores for object hypotheses provided by a nonlinear filter, whose state serves as memory. We test our methods on KITTI and SceneNN datasets, and also introduce the VISMA dataset, which contains ground truth pose, point-cloud map, and object models, along with time-stamped inertial measurements.To reduce the drift of the visual-inertial SLAM system -- a building block of all the visual perception systems we have built, we introduce an efficient loop closure detection approach based on the idea of hierarchical pooling of image descriptors. We also open-sourced a full-fledged SLAM system equipped with mapping and loop closure capabilities. The code is publicly available at https://github.com/ucla-vision/xivo