Automated and Robust Organ Segmentation for 3D-based Internal dose Calculation

Purpose: In this work we address image segmentation within dosimetry using deep learning and make three main contributions: a) to extend and op- timize the architecture of an existing Convolutional Neural Network (CNN) in order to obtain a fast, robust and accurate Computed Tomography (CT) based organ segmentation method for kidneys and livers; b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; c) to evaluate dosimetry results obtained using automated organ segmentation in comparison to manual segmentation done by two independent experts. Methods: We adapted a performant deep learning approach using CT-images to calculate organ boundaries with sufficiently high and adequate accuracy and processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the ac- tivity values from quantitatively reconstructed SPECT images for ”volumet- ric”/3D dosimetry. The retrieved activities were used to perform dosimetry calculations considering the kidneys as source organ. Results: The computational expenses of the algorithm was adequate enough to be used in clinical daily routine, required minimum pre-processing and per- formed within an acceptable accuracy of 93 . 4% for liver segmentation and of 94 . 1% for kidney segmentation. Additionally, kidney self-absorbed doses calcu- lated using automated segmentation differed 6 . 3% from dosimetries performed by two medical physicists in 8 patients. Conclusion: The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radio-pharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmen- tation methodology based on CT images accelerates the organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images.Trial registration: EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13

Preference-based segmentation: A study of dish preferences among Vietnamese teenagers

The purpose of this paper is to evaluate the usefulness of prefer-ence-based segmentation in understanding food-related behavior among Vietnamese teenagers. A sample of 413 teenagers in second-ary and high schools in three different regions is used. Their prefer-ences for 36 common Vietnamese dishes are evaluated. Four seg-ments based on their preferences are identified, including food likers (29%), poultry dislikers (27%), seafood dislikers (19%), and pork dislikers (25%). Differences between segments are profiled by a di-verse set of variables including consumption frequencies, food choice motives, attitudinal variables, and socio-demographic varia-bles. Dish preferences appear to be an appropriate basis for segmen-tation of Vietnamese adolescents. The differences found across the clusters for the differentiating variables can provide the basis for developing marketing strategies to target different segments, and also theoretical and practical implications are accordingly discussed.

An Effective and Rapid Algorithm for Segmenting CT Sequence Images

An algorithm of sequence medical images segmen- tation is proposed based on the combination of snakes algorithm and contour interpolation algorithm. Firstly, this algorithm uses snakes algorithm to segment the key layers in which target area change drastically. Then the algorithm calculates the position of reference points in the middle layers with the contour interpolation algorithm. Finally, snakes algorithm is applied again to segment the middle layers. Thus the segmentation of the sequence medical images are accomplished automatically. The experiments showed that the algorithm can obtain the boundary of the desired object from a sequence of medical images quickly and reliably.