We report a case where colonoscopic ultrasound was used to evaluate the pancreas. In this case the usual method of evaluating the body of the pancreas by upper gastrointestinal ultrasound was unsuccessful because of the presence of a large hiatal hernia. The other abdominal organs evaluated by colonoscopic ultrasound included the ileo-cecal valve, kidney, liver spleen and prostate. To our knowledge this is the first case where ultrasonic colonoscope has been used to evaluate the body of the pancreas.

À cause de difficultés d’observations, les recherches passées en biomécanique de l’abdomen soumis aux chocs se sont essentiellement limitées à la description de comportements externes. Cette étude s’intéresse au comportement interne d’organes abdominaux à l’aide de techniques récentes : l’échographie ultrarapide et l’élastographie par ondes de cisaillement. Tout d’abord, l’effet de conditions de perfusion sur la géométrie et le module de cisaillement interne de reins de porc ex vivo a été évalué. L’effet considérable de la pression appliquée a été observé, avec 80mmHg en artère conduisant à l’état le proche de l’in vivo. Ensuite, à l’aide de l’échographie ultrarapide, les comportements internes de reins porcins et humains dans cet état de référence ont été observés lors de compressions à des vitesses entre 0.08 et 8 s-1. Si pour le porc, la partie centrale (bassinet) se déforme plus, le rein humain a semblé avoir une déformation plus homogène. Enfin, à partir des résultats, un nouveau protocole a permis d’observer les comportements du côlon et du foie in situ lors d’impacts sur trois sujets d’anatomie. Dans l’ensemble, cette étude montre ainsi la possibilité de quantifier la relation entre chargement externe et interne grâce à l’échographie ultrarapide lors d’impacts / Due to limitations of observation techniques, past researches in impact biomechanics on the abdomen have been mostly limited to the description of the externals responses. This study focuses on the internal response of abdominal organs using recent observation techniques: ultrafast ultrasound imaging and shearwave elastography. First, the effects of perfusion conditions on the geometrical and internal shear moduli of ex vivo porcine kidneys were evaluated. The considerable effect of the applied pressure was observed, with 80mmHg in artery being closest to the in vivo state. Then, the internal responses of porcine and human kidneys were observed during compressions (rates: 0.08 to 8s-1). If in the porcine specimen the central part (pelvis) deformed the most, the human kidney seemed to have a more homogenous response. Finally, a protocol was developed to observe the responses of the colon and the liver in situ during impacts performed on three post mortem human subjects. Overall, this study demonstrates the possibility to establish a link between external and internal responses during impact using ultrafast ultrasound imaging

La fusion d’images TDM de phase artérielles et veineuses est cruciale afin d’assurer une meilleure planification chirurgicale. Cependant, le recalage non-rigide d’images abdominales est encore un challenge à cause de la respiration qui fait glisser les viscères abdominaux le long de la paroi abdominale, créant ainsi un champ de déformation discontinu. L’objectif de cette thèse est de fournir un outil de recalage précis pour les images TDM multi-phases de l’abdomen.Comme la zone de glissement dans l’abdomen est difficile à segmenter, nous avons d’abord implémenté deux outils de segmentation interactifs permettant une délinéation en 10 minutes de la paroi abdominale et du diaphragme. Pour intégrer ces zones de glissement comme a priori, nous réalisons le recalage sur de nouvelles images dans lesquelles la paroi abdominale et les viscères thoraciques ont été enlevés. Les évaluations sur des données de patient ont montré que notre approche fournit une précision d’environ 1 mm. / The fusion of arterial and venous phase CT images of the entire abdominal viscera is critical for a better diagnosis, surgi-cal planning and treatment, since these two phase images contain complementary information. However, non-rigid regis-tration of abdominal images is still a big challenge due to the breathing motion, which causes sliding motion between the abdominal viscera and the abdo-thoracic wall. The purpose of this thesis is to provide an accurate registration method for abdominal viscera between venous and arterial phase CT images.In order to remove the sliding motion effect, we decide to separate the image into big motion and less motion regions, and perform the registration on new images where abdo-thoracic wall and thoracic viscera are removed. The segmentation of these sliding interfaces is completed with our fast interactive tools within 10 minitues. Two state-of-the-art non-rigid registration algorithms are then applied on these new images and compared to registration obtained with original images. The evaluation using four abdominal organs (liver, kidney, spleen) and several vessel bifurcations shows that our approach provides a much higher accuracy within 1 mm.

Deep Learning techniques are widely used across various medical imaging applications. However, they are often fine-tuned for a specific modality and are not generalizable when it comes to new modalities or datasets. One of the main reasons for this is large data variations for e.g., the dynamic range of intensity values is large across multi-modal images. The goal of the project is to develop a method to address multi-modal learning that aims at segmenting liver from Computed Tomography (CT) images and abdominal organs from Magnetic Resonance (MR) images using deep learning techniques. In this project, a self-supervised approach is adapted to attain domain adaptation across images while retaining important 3D information from medical images using a simple 3D-UNet with a few auxiliary tasks. The method comprises of two main steps: representation learning via self-supervised learning (pre-training) and fully supervised learning (fine-tuning). Pre-training is done using a 3D-UNet as a base model along with some auxiliary data augmentation tasks to learn representation through texture, geometry and appearances. The second step is fine-tuning the same network, without the auxiliary tasks, to perform the segmentation tasks on CT and MR images. The annotations of all organs are not available in both modalities. Thus the first step is used to learn general representation from both image modalities; while the second step helps to fine-tune the representations to the available annotations of each modality. Results obtained for each modality were submitted online, and one of the evaluations obtained was in the form of DICE score. The results acquired showed that the highest DICE score of 0.966 was obtained for CT liver prediction and highest DICE score of 0.7 for MRI abdominal segmentation. This project shows the potential to achieve desired results by combining both self and fully-supervised approaches.