Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability.

BACKGROUND: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS: Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). CONCLUSIONS: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets

Case-Managers and integrated care

International audienceThis chapter on case management starts with a case story about Julia, a person with dementia, and her case manager, John (Sect. 4.1). It shows six innovations which are necessary to introduce case managers. Julia and John live in the year 2025, in a rich western country with a health system that supports integrated care by means of adequate financing and digitalization of care. Section 4.2 introduces a definition of the concept of case management and discusses important terms in it. Then (Sect. 4.3), two specific competences of case managers are discussed: (1) the assessments of care and social needs and (2) empowering interviewing of clients. The chapter continues (Sect. 4.4) with the comparison of the “ideal world” in the case story in 2025 with the real world in 2015 by focusing on case management practices in The Netherlands and France. The chapter ends (Sect. 4.5) by offering theories to support the implementation of the case manager. The chapter emphasises that case managers are not only for clients with dementia but are relevant as an approach to support other people with health, educational and financial problems; clients with developmental delays; patients with severe mental illness; patients with cancer and metastases; and persons with more than one chronic condition. In this chapter, the words clients, patients and persons are used as synonyms occurring in different care contexts