Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation.

PURPOSE: This work presents our contribution to one of the data challenges organized by the French Radiology Society during the Journées Francophones de Radiologie. This challenge consisted in segmenting the kidney cortex from coronal computed tomography (CT) images, cropped around the cortex. MATERIALS AND METHODS: We chose to train an ensemble of fully-convolutional networks and to aggregate their prediction at test time to perform the segmentation. An image database was made available in 3 batches. A first training batch of 250 images with segmentation masks was provided by the challenge organizers one month before the conference. An additional training batch of 247 pairs was shared when the conference began. Participants were ranked using a Dice score. RESULTS: The segmentation results of our algorithm match the renal cortex with a good precision. Our strategy yielded a Dice score of 0.867, ranking us first in the data challenge. CONCLUSION: The proposed solution provides robust and accurate automatic segmentations of the renal cortex in CT images although the precision of the provided reference segmentations seemed to set a low upper bound on the numerical performance. However, this process should be applied in 3D to quantify the renal cortex volume, which would require a marked labelling effort to train the networks.

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI.

PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ultrasound, computed tomography (CT) and MRI patient images; (2) build a network including radiologists, researchers, start-ups, large companies, and students from engineering schools, and; (3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018. MATERIALS AND METHODS: Relevant clinical questions were chosen by the Société Francaise de Radiologie. The challenge was designed to respect all French ethical and data protection constraints. Multidisciplinary teams with at least one radiologist, one engineering student, and a company and/or research lab were gathered using different networks, and clinical databases were created accordingly. RESULTS: Five challenges were launched: detection of meniscal tears on MRI, segmentation of renal cortex on CT, detection and characterization of liver lesions on ultrasound, detection of breast lesions on MRI, and characterization of thyroid cartilage lesions on CT. A total of 5,170 images within 4 months were provided for the challenge by 46 radiology services. Twenty-six multidisciplinary teams with 181 contestants worked for one month on the challenges. Three challenges, meniscal tears, renal cortex, and liver lesions, resulted in an accuracy>90%. The fourth challenge (breast) reached 82% and the lastone (thyroid) 70%. CONCLUSION: Theses five challenges were able to gather a large community of radiologists, engineers, researchers, and companies in a very short period of time. The accurate results of three of the five modalities suggest that artificial intelligence is a promising tool in these radiology modalities.

[How to prevent surgical morbidity after a total thyroidectomy for a multinodular euthyroid goiter?]. / Comment prévenir la morbidité chirurgicale de la thyroïdectomie totale pour goitre multinodulaire euthyroïdien?

AIM OF THE STUDY: Total thyroidectomy has been advocated for the treatment of multinodular nontoxic and benign goiter. The aim of this study, based on our experience, was to define the surgical factors which permit to decrease morbidity related to total thyroidectomy for multinodular euthyroid benign goiter. METHODS AND MATERIALS: In a retrospective study performed between January 1996 and September 2000, all records of total thyroidectomy for initial treatment of multinodular euthyroid benign goiter were reviewed. This study allowed to specify recurrent and parathyroid morbidity after surgery. RESULTS: There were 51 women and 13 men with a mean age of 47 years. Recurrent laryngeal nerve injury occurred in 2 patients. It resolved in 1 patient but was permanent in another (1.6%). Transient hypocalcemia was found in 8 patients (12.5%). One patient had permanent hypocalcemia (1.6%). CONCLUSION: The results of our serie are comparable to previous reports. Systematic identification of the recurrent laryngeal nerve, and preservation of the parathyroid blood supply permit to decrease the surgical morbidity.