Polyp Segmentation Using a Hybrid Vision Transformer and a Hybrid Loss Function.

Accurate and early detection of precursor adenomatous polyps and their removal at the early stage can significantly decrease the mortality rate and the occurrence of the disease since most colorectal cancer evolve from adenomatous polyps. However, accurate detection and segmentation of the polyps by doctors are difficult mainly these factors: (i) quality of the screening of the polyps with colonoscopy depends on the imaging quality and the experience of the doctors; (ii) visual inspection by doctors is time-consuming, burdensome, and tiring; (iii) prolonged visual inspections can lead to polyps being missed even when the physician is experienced. To overcome these problems, computer-aided methods have been proposed.

Medical image data augmentation: techniques, comparisons and interpretations.

Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria.

Evaluation of denoising techniques to remove speckle and Gaussian noise from dermoscopy images.

Computerized methods provide analyses of skin lesions from dermoscopy images automatically. However, the images acquired from dermoscopy devices are noisy and cause low accuracy in automated methods. Therefore, various methods have been applied for denoising in the literature.

Diagnosis of skin diseases in the era of deep learning and mobile technology.

Efficient methods developed with deep learning in the last ten years have provided objectivity and high accuracy in the diagnosis of skin diseases. They also support accurate, cost-effective and timely treatment. In addition, they provide diagnoses without the need to touch patients, which is very desirable when the disease is contagious or the patients have another contagious disease.

Deep learning based classification of facial dermatological disorders.

Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one of the most appropriate areas in medicine for automated diagnosis from images using pattern recognition techniques to provide accurate, objective, early diagnosis and interventions. Also, automated techniques provide diagnosis without depending on location and time.

Diagnosis of Alzheimer's disease with Sobolev gradient-based optimization and 3D convolutional neural network.

Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease.

Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma.

Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies.

Quantitative validation of anti-PTBP1 antibody for diagnostic neuropathology use: Image analysis approach.

Traditional diagnostic neuropathology relies on subjective interpretation of visual data obtained from a brightfield microscopy. This approach causes high variability, unsatisfactory reproducibility, and inability for multiplexing even among experts. These problems may affect patient outcomes and confound clinical decision-making.

Fully automated liver segmentation using Sobolev gradient-based level set evolution.

Quantitative analysis and precise measurements on the liver have vital importance for pre-evaluation of surgical operations and require high accuracy in liver segmentation from all slices in a data set. However, automated liver segmentation from medical image data sets is more challenging than segmentation of any other organ due to various reasons such as vascular structures in the liver, high variability of liver shapes, similar intensity values, and unclear edges between liver and its adjacent organs. In this study, a variational level set-based segmentation approach is proposed to be efficient in terms of processing time and accuracy.

Automatic labeling of portal and hepatic veins from MR images prior to liver transplantation.

Purpose: Living donated liver transplantation is an important task since a person (healthy donor) donates some part of her/his liver to a person in this surgery operation. The success of this operation mainly depends on the sufficiency of vessels and volume of the liver. Accurate labeling of portal and hepatic veins of donors reduces the incidence of complications during and after transplantation.

Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach.

The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing.

Quantification of liver fat: A comprehensive review.

Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor.

Fully automated liver segmentation from SPIR image series.

Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques.