Strumal Carcinoid of the Ovary: A Report of Two Cases

We describe two cases of strumal carcinoid of ovary, which is an extremely rare ovarian germ cell tumor composed of an intimate mixture of thyroid and carcinoid tissues. The first case involved a 63-year-old woman, who presented with a 1-month history of abdominal distension and unintentional weight loss (5 kg). Abdominal and chest computed tomography revealed right ovarian tumor and bilateral pleural effusion. Her thyroid-stimulating hormone level was slightly lower 0.475 μIU/mL (normal range: 0.55 - 4.78 μIU/mL);however, the levels of FT3 and FT4 were normal. Intraoperatively, the right ovary was enlarged (8.0 cm × 7.0 cm), with a smooth surface and intact capsule. Bilateral salpingo-oophorectomy with a total abdominal hysterectomy and an appendectomy was performed. The second case was a 54-year-old woman, in whom pelvic mass was recognized 2 months before. Intraoperatively, the left ovary was dumbbell-shaped (20.0 cm × 9.0 cm × 9.0 cm) and connected to two masses (9.0 cm × 8.0 cm and 8.0 cm × 7.0 cm, respectively). Her thyroid function test was normal (0.70 μIU/mL). Total abdominal hysterectomy, partial omentectomy, and pelvic adhesion lysis were performed. In both patients, intraoperative frozen section suggested strumal carcinoid of the ovary (stage IA). Ovarian strumal carcinoid should be included in a differential diagnosis of adnexal masses. The prognosis is considered good.

Development of a computer vision system to detect inactivity in group-housed pigs

Excessive inactivity in farm animals can be an early indication of illness.Traditional way for detecting excessive inactivity in pigs relies on manual inspection which can be laborious and especially time-consuming.This paper proposed a computer vision system that could detect inactivity of individual pigs housed in group pens which is potential in alarming the farmer of the animals concerned.The system recorded sequential depth images for the animals in a pen and implemented a proposed image processing and logic analysis scheme named as‘DepInact’to keep track of the inactive time of group-housed individual pigs over time.To verify the robustness and accuracy of the developed system,a total of 656 pairs of corresponding depth data and color images,consecutively taken 4 s apart from each other,were attained.The verification process involved manually identifying all pigs using the color images captured.The results of identification of all pigs that were inactive for more than the preset period of time by DepInact were compared to those by manual inspection through the color images captured.An accuracy of 85.7%was achieved using the verification data,thus demonstrating that the developed system is a viable alternative to manual detection of inactivity of group-housed pigs.Nevertheless,more research is still needed to improve the accuracy of the developed system.