Gallbladder volvulus is an uncommon condition that mostly affects older women. The cause of gallbladder volvulus is unknown, although intraoperative evidence of a floating gallbladder with a twisting of its pedicle, resulting in gallbladder ischemia, may lead to subsequent complications. Gallbladder volvulus symptoms are similar to acute cholecystitis, leading to delayed diagnosis and treatment. Early detection and prompt surgical intervention are critical for reducing morbidity and mortality. Even though numerous case reports have been published since 1898, gallbladder volvulus remains challenging to diagnose preoperatively. As a result, a high level of suspicion is required to prompt cholecystectomy and avoid further complications. We review the etiology, pathophysiology, clinical manifestation, diagnostic strategies, and treatment of this disease.

Canal of Nuck abnormality is a rare surgical condition. The pathologies are mostly encountered in young girls, less than five years of age. The incidence is even less in adults. Various pathologic conditions related to the failure of processus vaginalis obliteration can occur, involving herniation of intraabdominal structures including intestinal and genital contents such as the uterus, fallopian tube, and ovary and hydrocele of the canal of Nuck. According to its rarity, hydrocele of canal of Nuck is often misdiagnosed for common groin masses. This review summarizes and simplifies embryology, the pathophysiology of the canal of Nuck abnormalities, imaging findings, and treatment options with emphasis on the hydrocele.

Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

The incidence of liver cancer has grown in the past decade, with 905,677 new cases and 830,180 deaths in 2020. According to the highest annual fatality ratio, liver cancer is the third-leading caus...

Successful liver resection relies on accurate estimation of future liver remnant volume (FLRV). According to Couinaud's scheme, a liver is composed of eight functionally independent segments, each of which has its own vascular in- and out-flow tracks. Segmenting a liver by this scheme is vital to postoperative regeneration and hence prognosis outcome. Conventionally, estimation of liver segments was often done by hand on 3D computed tomography. The process is generally tedious, time consuming, and prone to observer variability. Alternatively, computerized methods had been proposed but impeded by anatomically irrelevant approximation and manually specified markers. To resolve the issues, this paper presents a novel method for functional liver segmentation. Its main contribution was performing analyses of differential geometry directly on a liver surface and interior venous system. Except for a few points being placed on major vessels, anatomical references required for defining all separating surfaces were automatically identified. To demonstrate its merits, virtual liver resection was implemented on the standard MICCAI SLIVER07 dataset, and the resultant segments were benchmarked against four most related works. Visual and numerical assessments reported herein indicated that our method could faithfully label all Couinaud's segments, especially the caudate, with lesser degree of user interaction. The preliminary findings suggested that it can be integrated into augmented surgical planning and intervention.