Muscle Segmentation for Orthopedic Interventions.

Skeletal muscle segmentation techniques can help orthopedic interventions in various scenes. In this chapter, we describe two methods of skeletal muscle segmentation on 3D CT images. The first method is based on a computational anatomical model, and the second method is a deep learning-based method. The computational anatomy-based methods are modeling the muscle shape with its running and use it for segmentation. In the deep learning-based methods, the muscle regions are directly acquired automatically. Both approaches can obtain muscle regions including shape, area, volume, and some other image texture features. And it is desirable that the method be selected by the required orthopedic intervention. Here, we show each design philosophy and features of a representative method. We discuss the various examples of site-specific segmentation of skeletal muscle in non-contrast images using torso CT and whole-body CT including in cervical, thoracoabdominal, surface and deep muscles. And we also mention the possibility of application to orthopedic intervention.

Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications.

PURPOSE: To develop and validate a fully automatic method for segmentation of paraspinal muscles from 3D torso CT images. METHODS: We propose a novel learning-based method to address this challenging problem. Multi-scale iterative random forest classifications with multi-source information are employed in this study to speed up the segmentation and to improve the accuracy. Here, multi-source images include the original torso CT images and later also the iteratively estimated and refined probability maps of the paraspinal muscles. We validated our method on 20 torso CT data with associated manual segmentation. We randomly partitioned the 20 CT data into two evenly distributed groups and took one group as the training data and the other group as the test data. RESULTS: The proposed method achieved a mean Dice coefficient of 93.0%. It took on average 46.5 s to segment a 3D torso CT image with the size ranging from [Formula: see text] voxels to [Formula: see text] voxels. CONCLUSIONS: Our fully automatic, learning-based method can accurately segment paraspinal muscles from 3D torso CT images. It generates segmentation results that are better than those achieved by the state-of-the-art methods.

Current status of HTLV-1 carrier in Japanese pregnant women.

We examined the current status of human T-cell leukemia virus type 1 (HTLV-1) carrier in Japanese pregnant women, according to the results of HTLV-1 screening and confirmation tests of women who gave birth in Japan in 2011. We requested 2642 obstetrical facilities to provide information of HTLV-1 tests and 71.3% of them responded. Considering the response rate and the rate of implementation of confirmation tests, the number of HTLV-1 carrier in Japanese pregnant women was estimated to be 1620 (0.16%) per year.

[Combined irinotecan (CPT-11) and nedaplatin (254-S) therapy for advanced and/or recurrent cervical cancer].

We performed combination therapy with irinotecan (CPT-11) plus nedaplatin (254-S) for patients with cervical cancer. A total of 9 patients with cervical cancer (5 patients treated in neoadjuvant setting and 4 patients for recurrent disease) were administered 80 mg/m(2) of 254-Sintravenously on day 1 and 50 mg/m(2) of CPT-11 intravenously on day 1, 8 and 15. Treatment was repeated every 4 weeks. The average number of courses administered was 3.8 (range, 2-6). Grade 3 or 4 adverse events were leukopenia in 4 patients, thrombocytopenia in 1 patient and vomiting in 1 patient. The response rate was 40% in the neoadjuvant setting (2 PRs) and 75% in recurrent disease (3 PRs).