In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, resulting in a lack of comparative evaluation studies on a common benchmark. To address this scientific gap and encourage deep learning research in the field, the ToothFairy challenge was organized within the MICCAI 2023 conference. In this context, a public dataset was released to also serve as a benchmark for future research. The dataset comprises 443 CBCT scans, with voxel-level annotations of the IAC available for 153 of them, making it the largest publicly available dataset of its kind. The participants of the challenge were tasked with developing an algorithm to accurately identify the IAC using the 2D and 3D-annotated scans. This paper presents the details of the challenge and the contributions made by the most promising methods proposed by the participants. It represents the first comprehensive comparative evaluation of IAC segmentation methods on a common benchmark dataset, providing insights into the current state-of-the-art algorithms and outlining future research directions. Furthermore, to ensure reproducibility and promote future developments, an open-source repository that collects the implementations of the best submissions was released.

Video-based activity and behavior analysis of mice has garnered wide attention in biomedical research. Animal facilities hold large numbers of mice housed in "home-cages" densely stored within ventilated racks. Automated analysis of mice activity in their home-cages can provide a new set of sensitive measures for detecting abnormalities and time-resolved deviation from the baseline behavior. Large-scale monitoring in animal facilities requires minimal footprint hardware that integrates seamlessly with the ventilated racks. The compactness of hardware imposes the use of fisheye lenses positioned in close proximity to the cage. In this paper, we propose a systematic approach to accurately estimate the 3D pose of the mouse from single-monocular fisheye-distorted images. Our approach employs a novel adaptation of a structured forest algorithm. We benchmark our algorithm against existing methods. We demonstrate the utility of the pose estimates in predicting mouse behavior in a continuous video.

Video-based activity and behavior analysis for mice has garnered wide attention in biomedical research. Animal facilities hold large numbers of mice housed in `home-cages' densely stored within ventilated racks. Automated analysis of mice activity in their home-cages can provide a new set of sensitive measures for detecting abnormalities and time-resolved deviation from baseline behavior. Large scale monitoring in animal facilities requires minimal footprint hardware that integrates seamlessly with the ventilated racks. Compactness of hardware imposes use of fisheye lenses positioned in close proximity to the cage. In this paper, we estimate the 3D pose of a mouse from fisheye distorted monocular monochromatic images using a novel adaptation of a structured forests algorithm. The method utilizes classification decision trees leveraging their versatility to store arbitrary information in the leaf-nodes. During training, the samples arriving at each node are mapped from continuous pose space to discrete class labels such that similar poses are grouped in the same class. The node splitting function is trained by optimizing a classification objective function rather than a high-dimensional regression one. The leaf-nodes store the pose parameters for the set of samples reaching the node. A prediction model preserving the structural relationship of the pose is formed based on the samples in the leaf-nodes. We apply the method to what we believe is the first known training set for 3D recovery of mouse key points from monocular images. We compare the results of our approach to those obtained via standard regression techniques.

Numerous 'non-maximum suppression' (NMS) post-processing schemes have been proposed for merging multiple independent object detections.We propose a generalization of NMS beyond bounding boxes to merge multiple pose estimates in a single frame.The final estimates are centroids rather than medoids as in standard NMS, thus being more accurate than any of the individual candidates.Using the same mathematical framework, we extend our approach to the multi-frame setting, merging multiple independent pose estimates across space and time and outputting both the number and pose of the objects present in a scene.Our approach sidesteps many of the inherent challenges associated with full tracking (e.g.objects entering/leaving a scene, extended periods of occlusion, etc.).We show its versatility by applying it to two distinct state-of-the-art pose estimation algorithms in three domains: human bodies, faces and mice.Our approach improves both detection accuracy (by helping disambiguate correspondences) as well as pose estimation quality and is computationally efficient.

Human faces captured in real-world conditions present large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food). Current face landmark estimation approaches struggle under such conditions since they fail to provide a principled way of handling outliers. We propose a novel method, called Robust Cascaded Pose Regression (RCPR) which reduces exposure to outliers by detecting occlusions explicitly and using robust shape-indexed features. We show that RCPR improves on previous landmark estimation methods on three popular face datasets (LFPW, LFW and HELEN). We further explore RCPR's performance by introducing a novel face dataset focused on occlusion, composed of 1,007 faces presenting a wide range of occlusion patterns. RCPR reduces failure cases by half on all four datasets, at the same time as it detects face occlusions with a 80/40% precision/recall.

We present a novel method for analyzing social behavior. Continuous videos are segmented into action `bouts' by building a temporal context model that combines features from spatio-temporal energy and agent trajectories. The method is tested on an unprecedented dataset of videos of interacting pairs of mice, which was collected as part of a state-of-the-art neurophysiological study of behavior. The dataset comprises over 88 hours (8 million frames) of annotated videos. We find that our novel trajectory features, used in a discriminative framework, are more informative than widely used spatio-temporal features; furthermore, temporal context plays an important role for action recognition in continuous videos. Our approach may be seen as a baseline method on this dataset, reaching a mean recognition rate of 61.2% compared to the expert's agreement rate of about 70%.

The development of image processing methods to discriminate between weed, crop and soil is an important step for precision agriculture, the main goal of which is the site-specific management of crops. The main challenge in terms of image analysis is to achieve an appropriate discrimination in outdoor field images under varying conditions of lighting, soil background texture and crop damage. This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil in digital images of a crop field. These methods are interchangeable among them, having each one of them a set of input parameters that need to be adjusted differently for each image. Two different evolutionary methods (standard genetic algorithm and NSGA-II) have been used to adjust these parameters and find the best method combinations. The proposed approach can reach a correlation with real data of up to 97% for a set of images acquired from different fields and under different conditions.