Detecting the Occluding Contours of the Uterus to Automatise Augmented Laparoscopy: Score, Loss, Dataset, Evaluation and User-Study - Archive ouverte HAL Access content directly
Journal Articles International Journal of Computer Assisted Radiology and Surgery Year : 2020

Detecting the Occluding Contours of the Uterus to Automatise Augmented Laparoscopy: Score, Loss, Dataset, Evaluation and User-Study

Abstract

Purpose. The registration of a preoperative 3D model, reconstructed for example from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation: they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the non-trivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery. Methods. Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end-to-end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector. Results. Evaluation shows that the proposed detector has a similar false negative rate to existing methods but substantially reduces both false positive rate and response thickness. Finally, we ran a user-study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 minutes and 53 seconds in surgeon time without sacrificing registration accuracy. Conclusions. We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state of the art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible. 2 T. François et al. Registration Preoperative 3D model with internal structures Occluding contours deformation of the 3D model Occluding contours Occlusion boundaries Uterus Connection contours Fig. 1 (left) In augmented laparoscopy, the preoperative 3D model is registered by fitting the occluding contours of the organ in laparoscopy images. The current systems require the surgeon to mark these contours manually during surgery. (right) An occluding contour arises at an organ boundary where the organ occludes another structure, as opposed to an occlusion boundary where the organ is occluded by another structure. The set of occluding contours is the silhouette. OC2D is the task of detecting the occluding contours for a specific object, here the uterus. It forms a task of semantic detection far more challenging than organ segmentation.
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Dates and versions

hal-02884670 , version 1 (30-06-2020)

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Tom François, Lilian Calvet, Sabrina Madad zadeh, Damien Saboul, Simone Gasparini, et al.. Detecting the Occluding Contours of the Uterus to Automatise Augmented Laparoscopy: Score, Loss, Dataset, Evaluation and User-Study. International Journal of Computer Assisted Radiology and Surgery, 2020, 15 (7), pp.1177-1186. ⟨10.1007/s11548-020-02151-w⟩. ⟨hal-02884670⟩
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