Appendectomy in endometriosis: an update on surgical indications and management of uncommon diseases.

PURPOSE OF REVIEW: Abnormal appendiceal disease is commonly encountered following an appendectomy when performed in patients with endometriosis. Appendiceal endometriosis is the most notable finding and can affect up to 39% of patients with endometriosis. Despite this knowledge, guidelines for performing an appendectomy have not been formally established. In this article, we review the surgical indications for an appendectomy at the time of endometriosis surgery and discuss the management of other diseases that may be encountered following the histopathologic evaluation of an excised appendix. RECENT FINDINGS: Removal of the appendix in patients with endometriosis contributes to optimal surgical management. Relying on abnormal appendiceal appearance for removal may leave endometriosis-affected appendices. For this reason, utilizing risk factors to guide surgical management is essential. Common appendiceal diseases are sufficiently managed with appendectomy. Uncommon diseases may require further surveillance. SUMMARY: Emerging data in our field support the performance of an appendectomy at the time of endometriosis surgery. Guidelines for performing a concurrent appendectomy should be formalized to encourage preoperative counselling and management for patients with risk factors for appendiceal endometriosis. Abnormal diseases is frequently encountered after appendectomy in the setting of endometriosis surgery and further management is based on the histopathology of the specimen.

Mapping high-fidelity volume rendering for medical imaging to CPU, GPU and many-core architectures.

Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intel architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.