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An important phase of radiation treatment planning is the accurate contouring of the organs at risk (OAR), which is necessary for the dose distribution calculation. The manual contouring approach currently used in clinical practice is tedious, time-consuming, and prone to inter and intra-observer variation. Therefore, a deep learning-based auto contouring tool can solve these issues by accurately delineating OARs on the computed tomography (CT) images. This paper proposes a two-stage deep learning-based segmentation model with an attention mechanism that automatically delineates OARs in thoracic CT images. After preprocessing the input CT volume, a 3D U-Net architecture will locate each organ to generate cropped images for the segmentation network. Next, two differently configured U-Net-based networks will perform the segmentation of large organs-left lung, right lung, heart, and small organs-esophagus and spinal cord, respectively. A post-processing step integrates all the individually-segmented organs to generate the final result. The suggested model outperformed the state-of-the-art approaches in terms of dice similarity coefficient (DSC) values for the lungs and the heart. It is worth mentioning that the proposed model achieved a dice score of 0.941, which is 1.1% higher than the best previous dice score, in the case of the heart, an important organ in the human body. Moreover, the clinical acceptance of the results is verified using dosimetric analysis. To delineate all five organs on a CT scan of size [Formula: see text], our model takes only 8.61 s. The proposed open-source automatic contouring tool can generate accurate contours in minimal time, consequently speeding up the treatment time and reducing the treatment cost., (© 2022. Australasian College of Physical Scientists and Engineers in Medicine.)

Referral patterns for bone mineral density testing by dual energy X-ray absorptiometry (DXA) scanning are seldom studied. In our study, the overall proportion of referrals from specialties remained low. This highlights the need for adequate utilisation of DXA by specialties treating subjects at risk for osteoporosis., Purpose/objectives: The knowledge of referral patterns for DXA scanning (dual energy X-ray absorptiometry) for bone mineral density (BMD) measurement is relevant in a developing country like India. We studied the referral source and clinical and densitometric profile of patients referred for DXA scanning at a south Indian tertiary care centre., Methods: We conducted a cross-sectional study over 3 months and included subjects referred during this period for BMD assessment (lumbar spine and femoral neck) by DXA scan. Details regarding referring departments and reasons for referral were collected. The number of patients seen in individual departments was obtained during the study period and respective proportions of patients referred were calculated., Results: Of the 1932 subjects included in the study, we observed a definite female preponderance (90.2%), with a mean (SD) age of 51.6 (13.3) years. The greatest number of referrals came from the departments of rheumatology (37%; n = 724) and endocrinology (20%; n = 382). Overall, 36% were referred for inflammatory arthritis or systemic inflammatory disorders (n = 696) and 34% for postmenopausal state screening (n = 657). In relation to the individual outpatient strength, the departments who referred the highest proportion of their patients were rheumatology (6.8%), endocrinology (1.76%) and geriatrics (1.05%). A diagnosis of osteoporosis at any one site was made in 41% (448 of 1107) and the BMD was below the expected range for age in 37% (304 of 825) of the referrals., Conclusion: Most referrals for DXA scanning were from rheumatology. Among the referred patients, about two fifth had osteoporosis and over one third had BMD below expected range for age. Although referrals by rheumatology were relatively higher, overall referrals from all departments remained low. This underscores the need for adequate utilisation of DXA by specialties treating subjects at risk for osteoporosis.