Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography

RATIONALE AND OBJECTIVES

To develop a computerized system for segmentation of bladder lesions on computed tomography urography (CTU) scans for detection and characterization of bladder cancer.

MATERIALS AND METHODS

We have developed an auto-initialized cascaded level set method to perform bladder lesion segmentation. The segmentation performance was evaluated on a preliminary dataset including 28 CTU scans from 28 patients collected retrospectively with institutional review board approval. The bladders were partially filled with intravenous contrast material. The lesions were located fully or partially within the contrast-enhanced area or in the non-contrast-enhanced area of the bladder. An experienced abdominal radiologist marked 28 lesions (14 malignant and 14 benign) with bounding boxes that served as input to the automated segmentation system and assigned a difficulty rating on a scale of 1 to 5 (5 = most subtle) to each lesion. The contours from automated segmentation were compared to three-dimensional contours manually drawn by the radiologist. Three performance metric measures were used for comparison. In addition, the automated segmentation quality was assessed by an expert panel of two experienced radiologists, who provided quality ratings of the contours on a scale from 1 to 10 (10 = excellent).

RESULTS

The average volume intersection ratio, the average absolute volume error, and the average distance measure were 67.2 ± 16.9%, 27.3 ± 26.9%, and 2.89 ± 1.69 mm, respectively. Of the 28 segmentations, 18 were given quality ratings of 8 or above. The average rating was 7.9 ± 1.5. The average quality ratings for lesions with difficulty ratings of 1, 2, 3, and 4 were 8.8 ± 0.9, 7.9 ± 1.8, 7.4 ± 0.9, and 6.6 ± 1.5, respectively.

CONCLUSION

Our preliminary study demonstrates the feasibility of using the three-dimensional level set method for segmenting bladder lesions in CTU scans.

3D segmentation of maxilla in cone-beam computed tomography imaging using base invariant wavelet active shape model on customized two-manifold topology

Recent advances in cone-beam computed tomography (CBCT) have rapidly enabled widepsread applications of dentomaxillofacial imaging and orthodontic practices in the past decades due to its low radiation dose, high spatial resolution, and accessibility. However, low contrast resolution in CBCT image has become its major limitation in building skull models. Intensive hand-segmentation is usually required to reconstruct the skull models. One of the regions affected by this limitation the most is the thin bone images. This paper presents a novel segmentation approach based on wavelet density model (WDM) for a particular interest in the outer surface of anterior wall of maxilla. Nineteen CBCT datasets are used to conduct two experiments. This mode-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 ± 0.2 mm of surface error from ground truth of bone surface.

Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography

Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.

DNA measurement of overlapping cell nuclei in thick tissue sections

The paper describes an improved image analysis procedure for measuring the DNA content of cell nuclei in thick sections of liver tissue by absorption densitometry. Whereas previous methods only permitted the analysis of isolated nuclei, the new technique enables both isolated and overlapping nuclei to be measured. A 3D segmentation procedure determines whether each object is an isolated nucleus or a pair of overlapping nuclei; in the latter case the combined optical density is redistributed to the individual nuclei. A selection procedure ensures that only complete nuclei are measured. The method has been tested on specially-prepared Feulgen-stained 20micro sections of normal liver tissue. The overall distribution of the nuclear DNA measurements shows well-defined diploid and tetraploid peaks, with coefficient of variations of less than 10%. Similar distributions were obtained from both the isolated nuclei and overlapped nuclei sub-populations.

VESSEL CENTERLINE TRACKING AND BOUNDARY SEGMENTATION IN CORONARY MRA WITH MINIMAL MANUAL INTERACTION

Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering proximal to mid segments of coronary arteries necessitates automatic analysis tools requiring minimal manual interactions to robustly measure cross-sectional area along the three-dimensional track of the arteries in under-sampled and non-isotropic datasets. In this work, we present a modular approach based on level set methods to track the vessel centerline, segment the vessel boundaries, and measure transversal area using two user-selected endpoints in each coronary of interest. Arterial area and vessel length are measured using our method and compared to the standard Soap-Bubble reformatting and analysis tool in in-vivo non-contrast enhanced coronary MRA images.

AI-Based 3D Liver Segmentation and Volumetric Analysis in Living Donor Data

This study investigated the application of deep learning for 3-dimensional (3D) liver segmentation and volumetric analysis in living donor liver transplantation. Using abdominal computed tomography data from 55 donors, this study aimed to evaluate the liver segmentation performance of various U-Net-based models, including 3D U-Net, RU-Net, DU-Net, and RDU-Net, before and after hepatectomy. Accurate liver volume measurement is critical in liver transplantation to ensure adequate functional recovery and minimize postoperative complications. The models were trained and validated using a fivefold cross-validation approach. Performance metrics such as Dice similarity coefficient (DSC), recall, specificity, precision, and accuracy were used to assess the segmentation results. The highest segmentation accuracy was achieved in preoperative images with a DSC of 95.73 ± 1.08%, while postoperative day 7 images showed the lowest performance with a DSC of 93.14 ± 2.10%. A volumetric analysis conducted to measure hepatic resection and regeneration rates revealed an average liver resection rate of 40.52 ± 8.89% and a regeneration rate of 13.50 ± 8.95% by postoperative day 63. A regression analysis was performed on the volumetric results of the artificial intelligence model's liver resection rate and regeneration rate, and all results were statistically significant at p < 0.0001. The results indicate high reliability and clinical applicability of deep learning models in accurately measuring liver volume and assessing regenerative capacity, thus enhancing the management and recovery of liver donors.

Morphologic Changes of the Intervertebral Disk During Growth

STUDY DESIGN

Cross-sectional.

OBJECTIVE

The aim of this study was to describe morphologic changes of the annulus fibrosus (AF) and nucleus pulposus (NP) in children during growth using magnetic resonance imaging.

SUMMARY OF BACKGROUND DATA

Little is known of intervertebral disk (IVD) maturation as opposed to degeneration, such as changes in relative AF/NP proportions and orientation during growth. Studies suggest that IVD plays a role in the etiology of pediatric spinal deformities. Therefore, understanding the morphologic development of the AF and NP during growth is key.

MATERIALS AND METHODS

An existing database of children aged 0 to 18 that had magnetic resonance imaging for indications unrelated to the spine were analyzed. The AF/NP were segmented semiautomatically from T1 to L5. The parameters: mean IVD height, cross-sectional area, slenderness (height/width ratio), volume (ratio), and relative position of the centroid of the NP within the IVD in three directions ( x , y , z ) were extracted, and compared between age, sex, and spinal level.

RESULTS

IVD height increased modestly and predominantly in the low-thoracic and lumbar spine during the first 5 to 10 years of life. Cross-sectional area and thus volume increased steadily at all levels throughout growth. IVD slenderness decreased sharply in the first years of life and remains relatively stable throughout the remainder of growth. IVDs were smaller and more slender in females, especially in the mid-thoracic spine at early adolescence. In the upper-thoracic and mid-thoracic spine the NP comprises 10% to 12% of total IVD volume during growth, this percentage increases in the low-thoracic and lumbar spine towards 20% to 25%. In the anterior-posterior direction, the position of the nucleus increasingly shifts with age, possibly in line with the developing sagittal profile of the spine.

CONCLUSION

This study describes the development of thoracic and lumbar IVDs during growth and may be used as a reference for future studies on the role of IVD in the etiology of disk-related disorders.