Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data

Reliable human pose estimation (HPE) is essential to many clinical applications, such as surgical workflow analysis, radiation safety monitoring and human-robot cooperation. Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room. Instead, we propose a novel approach based on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in real ORs. We extend the PS framework in two ways. First, we build robust and discriminative part detectors using both color and depth images. We also present a novel descriptor for depth images, called histogram of depth differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise constraints and a new method that makes exact inference tractable. Our approach is evaluated for pose estimation and clinician detection on a challenging RGB-D dataset recorded in a busy operating room during live surgeries. We conduct series of experiments to study the different part detectors in conjunction with the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D PS with RGB-D part detectors significantly improves the results in a visually challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg

Counting Sertoli Cells in Thin Testicular Tissue

This master thesis develops a novel system to model the tubular structure in thin sections of testicular tissue and count the Sertoli cells. A three-phase method is proposed to model the tubular structure in microscopic images of the tissue, the model is deployed to detect the cells. In the first phase, the germ-mass, which represents the inside layer of tubules, are detected. All cells are detected by radial symmetry transform and then the graph cut algorithm is used to separate the germ cells. Each region covered by a compact set of germ cells is considered as the germ-mass. In the second phase, all bright areas in the image are detected and used to adjust the germ-mass regions. In the last phase, all edges that are line-like are identified and straight lines are fitted to the edges. The lines are later connected to compensate for the broken parts of the tubules' boundaries. The closest cells to the germ-mass are chosen as the Sertoli cell candidates. The approximate boundary of tubules and the angle between the candidate cells are used to detect the Sertoli cells. Our experimental results show that our system is able to detect the tubule and the Sertoli cells with reasonable accuracy. If the method can not find enough edges to approximate the tubule's boundary, detecting Sertoli cells is complicated; the system can report those situations to the experts. Since we use the symmetry attribute of the cells to detect them, the method is quite robust against noise, artifacts, and non-uniform illumination. The method is able to capture all tubules, even tubules that do not have any bright region in the middle (lumen). To the best of my knowledge, no one has proposed a method to model tubular structure without lumen. The border approximation method can work well even for tubules that are partially in the image. It should be mentioned that the proposed method could be applied to model any tubular structure with one or more cells types

Détection et estimation 3D de la pose des personnes dans la salle opératoire à partir d'images RGB-D

In this thesis, we address the two problems of person detection and pose estimation in Operating Rooms (ORs), which are key ingredients in the development of surgical assistance applications. We perceive the OR using compact RGB-D cameras that can be conveniently integrated in the room. These sensors provide complementary information about the scene, which enables us to develop methods that can cope with numerous challenges present in the OR, e.g. clutter, textureless surfaces and occlusions. We present novel part-based approaches that take advantage of depth, multi-view and temporal information to construct robust human detection and pose estimation models. Evaluation is performed on new single- and multi-view datasets recorded in operating rooms. We demonstrate very promising results and show that our approaches outperform state-of-the-art methods on this challenging data acquired during real surgeries.Dans cette thèse, nous traitons des problèmes de la détection des personnes et de l'estimation de leurs poses dans la Salle Opératoire (SO), deux éléments clés pour le développement d'applications d'assistance chirurgicale. Nous percevons la salle grâce à des caméras RGB-D qui fournissent des informations visuelles complémentaires sur la scène. Ces informations permettent de développer des méthodes mieux adaptées aux difficultés propres aux SO, comme l'encombrement, les surfaces sans texture et les occlusions. Nous présentons des nouvelles approches qui tirent profit des informations temporelles, de profondeur et des vues multiples afin de construire des modèles robustes pour la détection des personnes et de leurs poses. Une évaluation est effectuée sur plusieurs jeux de données complexes enregistrés dans des salles opératoires avec une ou plusieurs caméras. Les résultats obtenus sont très prometteurs et montrent que nos approches surpassent les méthodes de l'état de l'art sur ces données cliniques

A generalizable approach for multi-view 3D human pose regression

Despite the significant improvement in the performance of monocular pose estimation approaches and their ability to generalize to unseen environments, multi-view (MV) approaches are often lagging behind in terms of accuracy and are specific to certain datasets. This is mainly due to the fact that (1) contrary to real world single-view (SV) datasets, MV datasets are often captured in controlled environments to collect precise 3D annotations, which do not cover all real world challenges, and (2) the model parameters are learned for specific camera setups. To alleviate these problems, we propose a two-stage approach to detect and estimate 3D human poses, which separates SV pose detection from MV 3D pose estimation. This separation enables us to utilize each dataset for the right task, i.e. SV datasets for constructing robust pose detection models and MV datasets for constructing precise MV 3D regression models. In addition, our 3D regression approach only requires 3D pose data and its projections to the views for building the model, hence removing the need for collecting annotated data from the test setup. Our approach can therefore be easily generalized to a new environment by simply projecting 3D poses into 2D during training according to the camera setup used at test time. As 2D poses are collected at test time using a SV pose detector, which might generate inaccurate detections, we model its characteristics and incorporate this information during training. We demonstrate that incorporating the detector's characteristics is important to build a robust 3D regression model and that the resulting regression model generalizes well to new MV environments. Our evaluation results show that our approach achieves competitive results on the Human3.6M dataset and significantly improves results on a MV clinical dataset that is the first MV dataset generated from live surgery recordings.Comment: The supplementary video is available at https://youtu.be/Cx_kTRzqqz

PATG: position-aware temporal graph networks for surgical phase recognition on laparoscopic videos

PURPOSE: We tackle the problem of online surgical phase recognition in laparoscopic procedures, which is key in developing context-aware supporting systems. We propose a novel approach to take temporal context in surgical videos into account by precise modeling of temporal neighborhoods. METHODS: We propose a two-stage model to perform phase recognition. A CNN model is used as a feature extractor to project RGB frames into a high-dimensional feature space. We introduce a novel paradigm for surgical phase recognition which utilizes graph neural networks to incorporate temporal information. Unlike recurrent neural networks and temporal convolution networks, our graph-based approach offers a more generic and flexible way for modeling temporal relationships. Each frame is a node in the graph, and the edges in the graph are used to define temporal connections among the nodes. The flexible configuration of temporal neighborhood comes at the price of losing temporal order. To mitigate this, our approach takes temporal orders into account by encoding frame positions, which is important to reliably predict surgical phases. RESULTS: Experiments are carried out on the public Cholec80 dataset that contains 80 annotated videos. The experimental results highlight the superior performance of the proposed approach compared to the state-of-the-art models on this dataset. CONCLUSION: A novel approach for formulating video-based surgical phase recognition is presented. The results indicate that temporal information can be incorporated using graph-based models, and positional encoding is important to efficiently utilize temporal information. Graph networks open possibilities to use evidence theory for uncertainty analysis in surgical phase recognition

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work