Preoperative Computed Tomography Morphological Features Indicative of Incisional Hernia Formation After Abdominal Surgery.

OBJECTIVE: To investigate key morphometric features identifiable on routine preoperative computed tomography (CT) imaging indicative of incisional hernia (IH) formation following abdominal surgery. BACKGROUND: IH is a pervasive surgical disease that impacts all surgical disciplines operating in the abdominopelvic region and affecting 13% of patients undergoing abdominal surgery. Despite the significant costs and disability associated with IH, there is an incomplete understanding of the pathophysiology of hernia. METHODS: A cohort of patients (n=21,501) that underwent colorectal surgery was identified, and clinical data and demographics were extracted, with a primary outcome of IH. Two datasets of case-control matched pairs were created for feature measurement, classification, and testing. Morphometric linear and volumetric measurements were extracted as features from anonymized preoperative abdominopelvic CT scans. Multivariate Pearson testing was performed to assess correlations among features. Each feature's ability to discriminate between classes was evaluated using 2-sided paired t testing. A support vector machine was implemented to determine the predictive accuracy of the features individually and in combination. RESULTS: Two hundred and twelve patients were analyzed (106 matched pairs). Of 117 features measured, 21 features were capable of discriminating between IH and non-IH patients. These features are categorized into three key pathophysiologic domains: 1) structural widening of the rectus complex, 2) increased visceral volume, 3) atrophy of abdominopelvic skeletal muscle. Individual prediction accuracy ranged from 0.69 to 0.78 for the top 3 features among 117. CONCLUSIONS: Three morphometric domains identifiable on routine preoperative CT imaging were associated with hernia: widening of the rectus complex, increased visceral volume, and body wall skeletal muscle atrophy. This work highlights an innovative pathophysiologic mechanism for IH formation hallmarked by increased intra-abdominal pressure and compromise of the rectus complex and abdominopelvic skeletal musculature.

Segmentation evaluation with sparse ground truth data: Simulating true segmentations as perfect/imperfect as those generated by humans.

Fully annotated data sets play important roles in medical image segmentation and evaluation. Expense and imprecision are the two main issues in generating ground truth (GT) segmentations. In this paper, in an attempt to overcome these two issues jointly, we propose a method, named SparseGT, which exploit variability among human segmenters to maximally save manual workload in GT generation for evaluating actual segmentations by algorithms. Pseudo ground truth (p-GT) segmentations are created by only a small fraction of workload and with human-level perfection/imperfection, and they can be used in practice as a substitute for fully manual GT in evaluating segmentation algorithms at the same precision. p-GT segmentations are generated by first selecting slices sparsely, where manual contouring is conducted only on these sparse slices, and subsequently filling segmentations on other slices automatically. By creating p-GT with different levels of sparseness, we determine the largest workload reduction achievable for each considered object, where the variability of the generated p-GT is statistically indistinguishable from inter-segmenter differences in full manual GT segmentations for that object. Furthermore, we investigate the segmentation evaluation errors introduced by variability in manual GT by applying p-GT in evaluation of actual segmentations by an algorithm. Experiments are conducted on ∼500 computed tomography (CT) studies involving six objects in two body regions, Head & Neck and Thorax, where optimal sparseness and corresponding evaluation errors are determined for each object and each strategy. Our results indicate that creating p-GT by the concatenated strategy of uniformly selecting sparse slices and filling segmentations via deep-learning (DL) network show highest manual workload reduction by ∼80-96% without sacrificing evaluation accuracy compared to fully manual GT. Nevertheless, other strategies also have obvious contributions in different situations. A non-uniform strategy for slice selection shows its advantage for objects with irregular shape change from slice to slice. An interpolation strategy for filling segmentations can achieve ∼60-90% of workload reduction in simulating human-level GT without the need of an actual training stage and shows potential in enlarging data sets for training p-GT generation networks. We conclude that not only over 90% reduction in workload is feasible without sacrificing evaluation accuracy but also the suitable strategy and the optimal sparseness level achievable for creating p-GT are object- and application-specific.

Thoracic Quantitative Dynamic MRI to Understand Developmental Changes in Normal Ventilatory Dynamics.

BACKGROUND: A database of normative quantitative measures of regional thoracic ventilatory dynamics, which is essential to understanding better thoracic growth and function in children, does not exist. RESEARCH QUESTION: How to quantify changes in the components of ventilatory pump dynamics during childhood via thoracic quantitative dynamic MRI (QdMRI)? STUDY DESIGN AND METHODS: Volumetric parameters were derived via 51 dynamic MRI scans for left and right lungs, hemidiaphragms, and hemichest walls during tidal breathing. Volume-based symmetry and functional coefficients were defined to compare left and right sides and to compare contributions of the hemidiaphragms and hemichest walls with tidal volumes (TVs). Statistical analyses were performed to compare volume components among four age-based groups. RESULTS: Right thoracic components were significantly larger than left thoracic components, with average ratios of 1.56 (95% CI, 1.41-1.70) for lung TV, 1.81 (95% CI, 1.60-2.03) for hemidiaphragm excursion TV, and 1.34 (95% CI, 1.21-1.47) for hemichest wall excursion TV. Right and left lung volumes at end-expiration showed, respectively, a 44% and 48% increase from group 2 (8 ≤ age < 10) to group 3 (10 ≤ age < 12). These numbers from group 3 to group 4 (12 ≤ age ≤ 14) were 24% and 28%, respectively. Right and left hemichest wall TVs exhibited, respectively, 48% and 45% increases from group 3 to group 4. INTERPRETATION: Normal right and left ventilatory volume components have considerable asymmetry in morphologic features and dynamics and change with age. Chest wall and diaphragm contributions vary in a likewise manner. Thoracic QdMRI can provide quantitative data to characterize the regional function and growth of the thorax as it relates to ventilation.

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models.

PURPOSE: Statistical object shape models (SOSMs), known as probabilistic atlases, are popular in medical image segmentation. They register an image into the atlas coordinate system, such that a desired object can be delineated from the constraints of its shape model. While this strategy facilitates segmenting objects with even weak-boundary contrast, it tends to require more models per object to cope with possible registration errors. Fuzzy object shape models (FOSMs) gain substantial speed by avoiding image registration and placing more relaxed model constraints with optimum object search. However, they tend to require stronger object boundary contrast for effective delineation. In this work, the authors show that optimum object search, the essential underpinning of FOSMs, can improve segmentation efficacy of SOSMs with fewer models per object. METHODS: For the sake of efficiency, the authors use three atlases per object (SOSM-3) as baseline for segmentation based on the best match with posterior probability maps. A novel strategy for SOSM with a single atlas and optimum object search (SOSM-S) is presented. When registering an image to the atlas system, one should expect that the object's boundary falls within the uncertainty region of the model-region wherein voxels show probabilities greater than 0 and less than 1 to be in the object. Since registration may fail, SOSM-S translates the atlas locally and, at each location, delineates and scores a candidate object in the uncertainty region. Segmentation is defined by the candidate with the highest score. The presented FOSM also uses a single model per object, but model construction uses only shape translations, building a fuzzy object model with larger uncertainty region. Optimum object search requires estimation of the object's location and/or optimization algorithms to speed-up segmentation. RESULTS: The authors evaluate SOSM-3, SOSM-S, and FOSM on 75 CT-images of the thorax and 35 MR T1-weighted images of the brain, with nine objects of interest. The results show that SOSM-S and FOSM can segment seven out of the nine objects with higher accuracy than SOSM-3, according to the average symmetric surface distance and statistical test. SOSM-S was consistently more accurate than FOSM, FOSM being 2-3 orders of magnitude faster than SOSM-S and SOSM-3 for model construction and hundreds of times faster than them for segmentation. CONCLUSIONS: Although multiple models per object can usually improve segmentation efficacy, the optimum object search has shown to reduce the number of required models. The efficiency gain of FOSM over SOSM-S motivates its use for interactive applications and studies with large image data sets. FOSM and SOSM impose different degrees of shape constraints from the model, making one approach more suitable than the other, depending on contrast. This suggests the use of hybrid models that can take advantage from the strengths of fuzzy and statistical models.