Herrick District Library

Snakebite accidents are very common nearby forest areas. The majority of these incidents are caused by snakes of the genus Bothrops, which comprises over 45 species distributed in Central and South America. These snakes usually hunt small mammals and lizards at dusk. Snakebites from this genus generally occur in the head, limbs, and neck regions. Bothrops snakes are considered aggressive animals, characterized by inverted "V" markings along their bodies, triangular heads, smooth tails, and length ranging from 0.5m to 2m. They exhibit color variations of brown, green, and gray. Bothrops venom has necrotic, coagulating, vasculotoxic, and nephrotoxic effects, which can cause extensive necrosis at the bite site, tissue loss, and limb dysfunction, sometimes resulting in amputation. The bitten animal may experience prostration, tachypnea, localized edema and pain, petechiae, hemorrhages, and extensive necrotic areas. Hematuria, hypotension, pulmonary edema, and even death, as well as various laboratory abnormalities, may also occur. The case reported herein was attended at a veterinary clinic in Campos Altos, Minas Gerais, Brazil. It involved a mixed-breed female dog, 3 years old, weighing 4.5 kg, which had been bitten by a Bothrops snake approximately 90 minutes before. The animal presented edema and pain at the site of the bite (pelvic limb), as well as tachycardia. At once, it was administered intravenous polyvalent antiophidic serum, dexamethasone, serum, furosemide, tramadol, and ceftriaxone (antibiotic). Local treatment included corticosteroid and antibiotic ointment dressings. The animal remained stable, but after 24 hours, an extensive area with edema, hematoma, bleeding, and early necrosis at the bite site was observed, worsening with each passing hour. After 48 hours, therapy with Bothrops CH6 (5 globules every 8 hours) was initiated to reduce local effects (edema, hemorrhage, and necrosis). The animal showed rapid improvement in the appearance of the lesion, and after 5 days of treatment, there was no longer edema or bleeding, only a circumscribed cutaneous injury, which was managed with local dressings (cleaning and healing ointment). The animal recovered well without any sequelae.

The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and instance segmentation and their respective application fields. Additionally, it introduces several well-known deep neural networks for segmenting medical images using deep learning. Regarding the models, this article introduces the structure and characteristics of each model as well as their respective advantages and disadvantages. This essay also introduces examples of deep learning using different deep neural network models to segment specific medical images, including image segmentation based on FCN for the heart and U-Net for the kidneys.