Pictorial review. Diagnosis of recurrent prostate cancer and its mimics at multiparametric prostate MRI.

Biochemical recurrence after treatment for prostate cancer (PCa) is a significant issue. Early diagnosis of local recurrence is important for making prompt treatment decisions and is strongly associated with patient prognosis. Without salvage therapy, the average time from development of local recurrence to distant metastasis is approximately 3 years. Biochemical recurrence does not differentiate local recurrence from systemic disease; there is no reliable way to clinically diagnose local recurrence. Recent advances in multiparametric MRI (mp-MRI) techniques have markedly improved detection of local recurrence following therapy. However, a wide variety of entities can mimic recurrent PCa at mp-MRI. Therefore, the purpose of this pictorial review is to discuss the MRI findings of locally recurrent PCa and its mimics, emphasizing the key MRI features that help to differentiate local recurrence from its mimics.

Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries.

Spleen segmentation is especially challenging as the majority of solid organs in the abdomen region have similar gray level range. Physician analysis of computed tomography (CT) images to assess abdominal trauma could be very time consuming and hence, automating this process can reduce time to treatment. The proposed method presented in this paper is a fully automated and knowledge based technique that employs anatomical information to accurately segment the spleen in CT images. The spleen detection procedure is proposed to locate the spleen in both healthy and injured cases. In the presence of hemorrhage and laceration, the edge merging technique is used. The accuracy of the method is measured by some criteria such as mis-segmented area, accuracy, specificity and sensitivity. The results show that the proposed spleen segmentation method performs well and outperforms other methods.

Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation.

This paper attempts to predict Intracranial Pressure (ICP) based on features extracted from non-invasively collected patient data. These features include midline shift measurement and textural features extracted from Computed axial Tomography (CT) images. A statistical analysis is performed to examine the relationship between ICP and midline shift. Machine learning is also applied to estimate ICP levels with a two-stage feature selection scheme. To avoid overfitting, all feature selections and parameter selections are performed using a nested 10-fold cross validation within the training data. The classification results demonstrate the effectiveness of the proposed method in ICP prediction.

Hemorrhage detection and segmentation in traumatic pelvic injuries.

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising.

An automated method for hemorrhage detection in traumatic pelvic injuries.

Hemorrhage is the main cause of deaths that occurs within first 24 hours after a traumatic pelvic injury. Therefore, it is very important to determine hemorrhage quickly. Hemorrhages are detected using a CT scan. However, it is very time consuming for physicians to look for hemorrhage in all CT slices. Therefore, an automated system is needed. This paper proposes an automated hemorrhage detection technique by incorporating anatomical information of pelvic region. The results showed method performs comparably to manual methods. A statistical test is conducted to see if the volume of hemorrhage detected using this technique is significantly different from the volume assessed manually.