This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Preoperative planning systems are commonly used for oral implant surgery. One of the objectives is to determine if the quantity and quality of bone is sufficient to sustain an implant while avoiding critical anatomic structures. We aim to automate the segmentation of jaw tissues on CT images: cortical bone, trabecular core and especially the mandibular canal containing the dental nerve. This nerve must be avoided during implant surgery to prevent lip numbness. Previous work in this field used thresholds or filters and needed manual initialization. An automated system based on the use of Active Appearance Models (AAMs) is proposed. Our contribution is a completely automated segmentation of tissues and a semi-automatic landmarking process necessary to create the AAM model. The AAM is trained using 215 images and tested with a leave-4-out scheme. Results obtained show an initialization error of 3.25% and a mean error of 1.63mm for the cortical bone, 2.90mm for the trabecular core, 4.76mm for the mandibular canal and 3.40mm for the dental nerve.

Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on manually locating specific landmarks, it is a tedious, time-consuming and error prone task. In this paper, we propose an automated system based on the use of Active Appearance Models (AAMs). Special attention has been paid to clinical validation of our method since previous work in this field used few images, was tested in the training set and/or did not take into account the variability of the images. In this research, a top-hat transformation was used to correct the intensity inhomogeneity of the radiographs generating a consistent training set that overcomes the above described drawbacks. The AAM was trained using 96 hand-annotated images and tested with a leave-one-out scheme obtaining an average accuracy of 2.48mm. Results show that AAM combined with mathematical morphology is the suitable method for clinical cephalometric applications. Radiographs provided by Y. Perrin, A. Sanpietro and the Eastman Dental Institute.

Thalidomide, mainly used for the treatment of leprosy, is a current teratogen in South America, and it is reasonable to assume that at present this situation is affecting many births in underdeveloped countries. Moreover, the potential re‐marketing of thalidomide for the treatment of a large variety of diseases may extend the problem to the developed world. When the drug is available, the control of its intake during early pregnancy is very difficult since most pregnancies are unintended. The ongoing occurrence of thalidomide embryopathy cases went undetected by the ECLAMC, due to several factors: (1) low populational coverage through this monitoring system; (2) pre‐existence of the teratogen with its effects present in both baseline (expected) and monitored (observed) materials; and (3) lack of a defined phenotype to be monitored. Thus, if thalidomide re‐enters the market throughout the world, due to the wide range of new applications, occurrence of phocomelia alone might not be sufficient to detect its effects. By a case‐reference approach, the ECLAMC registered 34 thalidomide embryopathy cases born in South America after 1965 whose birthplaces correspond to endemic areas for leprosy. Phocomelia was found in five of eleven fully described cases. Thus, phocomelia alone is neither specific nor sufficient to serve as a suitable phenotype to survey the teratogenic effects of thalidomide. Therefore, a thalidomide‐like phenotype, defined as any bilateral upper and/or lower limb reduction defect of the preaxial and/or phocomelia types, should be included in the routine surveillance of birth defects in all programmes. Teratology 54:273–277, 1996. © 1997 Wiley‐Liss, Inc.

No abstract