Zgodnje odkrivanje tveganja nenalezljivih bolezni pri bolnikih z rakom dojk na podlagi umetne inteligence (projekt ARTILLERY)

Naraščajoče zbolevanje in izboljšave pri zdravljenju bolnikov z rakom dojk prispevajo tudi k naraščajočemu številu preživelih, ki pa so bolj ogroženi za razvoj drugih kroničnih bolezni, kot so srčno-žilne bolezni, bolezni dihal, povečanje telesne mase in osteoporoza. Ker se večina bolnikov zdravi tudi z radioterapijo, lahko iz slik, pridobljenih z računalniško tomografijo za namene načrtovanja obsevanja, izluščimo tudi informacije o dejavnikih tveganja za druge bolezni. Cilj projekta ARTILLERY v sklopu okvirnega programa Evropske unije Obzorje Evropa je razvoj in vrednotenje zanesljivih in zaupanja vrednih računalniško podprtih sistemov umetne inteligence, ki jih bo mogoče uporabiti na rutinsko pridobljenih računalniško tomografskih slikah za odkrivanje kroničnih bolezni ali njihovih dejavnikov tveganja za namene podaljševanja pričakovane življenjske dobe in izboljšanja kakovosti življenja v naraščajoči populaciji preživelih bolnikov z rakom dojk. V Laboratoriju za slikovne tehnologije na Fakulteti za elektrotehniko Univerze v Ljubljani imamo dolgoletne izkušnje z razvojem in vrednotenjem računalniško podprtih metod za analizo medicinskih slik hrbtenice, zato je naša vloga pri projektu povezana z določanjem tveganja osteoporoze in osteopenije ter razpoznavanjem vretenčnih zlomov. Kljub temu, da je projekt šele v začetni fazi, smo razvili računalniško podprto metodo na osnovi umetne inteligence za segmentacijo hrbtenice in določanje področij zanimanja znotraj vretenčnih teles, na podlagi katerih bomo izluščili informacije o mineralni kostni gostoti ter jo povezali z referenčnimi diagnostičnimi izvidi. Rezultati začetnih raziskav kažejo, da bo razvita metoda potencialno uporabna za zgodnje odkrivanje osteoporoze in osteopenije ter za razpoznavanje vretenčnih zlomov

Design of Reverberation Chamber

V okviru naloge je bila zasnovana alfa komora za postavitev v Laboratorij za delovne stroje in tehnično akustiko na Fakulteti za strojništvo v Ljubljani. Alfa komora bo namenjena meritvam koeficienta absorpcije zvoka in zvočne moči majhnih naprav. Na podlagi pregleda literature in obstoječih rešitev so se identificirali in definirali ključni akustični parametri, geometrijske smernice in druge zahteve pri snovanje alfa komore. Na poenostavljenih 3D modelih je bila po metodi končnih elementov izvedena numerična analiza homogenosti distribucije lastnih frekvenc zvočnega polja v notranjem volumnu, na podlagi katere je bila na koncu določena geometrija komore. Na podlagi numerične analize frekvenčnega odziva nosilne konstrukcije ob vzbujanju z zvočnim signalom je bil izbran tudi optimalni material in dimenzije sestavnih elementov nosilne konstrukcije. Izdelan je bil podroben 3D model nosilne konstrukcije in pripravljena tehnična dokumentacija za jeklene nosilne elemente.Within this assignment, an alpha chamber was developed to be placed in the Laboratory of Working Machines and Technical Acoustics at the Faculty of Mechanical Engineering in Ljubljana. The alpha chamber will be used for sound absorption coefficient and small machines’ sound power measurements. Critical acoustic parameters, design guidelines and other objectives were identified and defined based on literature review and review of existing solutions. Simplified 3D models were used for finite element analysis of modal frequency distribution homogeneity in the inner volume of the chamber. Results of simulations represent the basis for the determination of the optimal sound field within the geometry of the chamber. Based on numerical analysis of load-carrying structure frequency response to sound signal excitation, optimal material and dimensions of load-carrying structure elements were determined. A detailed 3D model of load-carrying structure was constructed and technical documentation for steel load-carrying elements was prepared

Računalniško podprto merjenje parametrov sagitalne orientacije medenice iz rentgenskih slik

Izhodišča: Sagitalna orientacija medenice je pomemben element sagitalnega ravnovesja, kvantitativno pa jo lahko opredelimo na podlagi merjenja geometrijskih parametrov medenice, in sicer naklona križnične končne ploskve (SS), nagiba medenice (PT) in naklona medenice (PI). V tem članku predstavljamo rezultate popolnoma samodejnega računalniško podprtega merjenja parametrov sagitalne orientacije medenice na podlagi rentgenskih slik ter testiramo hipotezo, da ni statistično pomembnih razlik med dobljenimi in referenčnimi ročnimi meritvami. Metode: Samodejno računalniško podprto merjenje parametrov sagitalne orientacije medenice temelji na najnovejših tehnologijah iz področja obdelave in analize medicinskih slik, in sicer na konvolucijskih nevronskih mrežah kot posebni obliki tehnik globokega učenja. Na podlagi teh tehnologij se v sagitalni rentgenski sliki medenice najprej samodejno določijo območja zanimanja (križnična končna ploskev ter kolčni sklepni glavi), nato pa se znotraj teh območij določijo značilne točke, in sicer anteriorni rob, središče in posteriorni rob križnične končne ploskve, na katere se kasneje prilega premica, ter središči obeh kolčnih sklepnih glav s pripadajočo sredinsko točko, ki predstavlja os medenice. Na podlagi osi medenice ter premice vzdolž križnične končne ploskve in njenega središča lahko končno izračunamo SS, PT in PI. Rezultati: Merjenje je bilo retrospektivno opravljeno na sagitalnih rentgenskih slikah medenice 38 oseb (15 moških in 23 žensk; povprečna starost 71,1 let). Statistična analiza referenčnih ročnih in samodejnih računalniško podprtih meritev parametrov sagitalne orientacije medenice je pokazala na relativno dobro ujemanje in majhno odstopanje. Za SS, PT in PI je bila povprečna absolutna razlika (standardni odklon) namreč 5,2º (3,8º), 2,2º (2,0º) in 5,1º (4,4º), korelacijski koeficient 0,73, 0,94 in 0,82 (p 0,05). Zaključek: Rezultati so pokazali, da ni statistično pomembnih razlik med referenčnimi ročnimi ter samodejnimi računalniško podprtimi meritvami parametrov sagitalne orientacije medenice. Poleg tega so odstopanja od referenčnih ročnih meritev znotraj ponovljivosti in zanesljivosti samega ročnega določanja teh parametrov, zato je z samodejnim računalniško podprtim merjenjem mogoče natančno določiti parametre sagitalne orientacije medenice. Vsekakor pa pregleda in potrjevanja tako izmerjenih vrednosti ne smemo popolnoma opustiti, saj so lahko odstopanja v določenih primerih precej velika, predvsem zaradi naravne biološke variabilnosti človeške anatomije ter lastnosti rentgenskega slikanja

USE OF HYALURONAN-RICH TRANSFER MEDIUM FOR A SINGLE BLASTOCYST TRANSFER IN VITRO FERTILIZATION PROCEDURE

Background. The best way to avoid undesirable multiple pregnancies following in vitro fertilization procedure (IVF) is to perform elective single embryo transfer, but the procedure might result in a reduction of the pregnancy rates. Aim of our study was to establish whether a single blastocyst transfer using a hyaluronan rich transfer medium results in higher pregnancy rates in comparison to the transfer using a conventional transfer medium. Material and methods. Our prospective randomized study included 107 patients enrolled in the 1st, 2nd and 3rd classical IVF or intracytoplasmic sperm injection (ICSI) treatment attempt. Patients included were under 37 years of age with at least one blastocyst developed in the procedure. In the study group (47 patients) blastocyst transfers using the hyaluronan rich transfer medium were performed and in the control group (60 patients) the conventional medium was used. The pregnancy rates in the study and in the control group were compared. Results. The average pregnancy rate per single blastocyst transfer was 30 %; there were no twin pregnancies. The single blastocyst transfer using hyaluronan resulted in a non-significantly higher pregnancy rate (11 %). A significantly higher pregnancy rate with the use of hyaluronan was found in the subgroup of patients with two or more blastocysts developed in their 2nd and 3rd IVF attempt (p = 0.045). Conslusions. The single blastocyst transfer results in high implantation rates. Hyaluronan significantly contributes to higher implantation rates in a selected subgroup of patients following previous implantation failure and with multiple blastocysts developed

HaN-Seg

Purpose: For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning, however, existing approaches are focused on either computed tomography (CT) or magnetic resonance (MR) images, while multimodal segmentation has not been thoroughly explored yet. We present a dataset of CT and MR images of the same patients with curated reference HaN OAR segmentations for an objective evaluation of segmentation methods. Acquisition and validation methods: The cohort consists of HaN images of 56 patients that underwent both CT and T1-weighted MR imaging for image-guided RT. For each patient, reference segmentations of up to 30 OARs were obtained by experts performing manual pixel-wise image annotation. By maintaining the distribution of patient age and gender, and annotation type, the patients were randomly split into training Set 1 (42 cases or 75%) and test Set 2 (14 cases or 25%). Baseline auto-segmentation results are also provided by training the publicly available deep nnU-Net architecture on Set 1, and evaluating its performance on Set 2. Data format and usage notes: The data are publicly available through an open-access repository under the name HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Dataset. Images and reference segmentations are stored in the NRRD file format, where the OAR filenames correspond to the nomenclature recommended by the American Association of Physicists in Medicine, and OAR and demographics information is stored in separate comma-separated value files. Potential applications: The HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched in parallel with the dataset release to promote the development of automated techniques for OAR segmentation in the HaN. Other potential applications include out-of -challenge algorithm development and benchmarking, as well as external validation of the developed algorithms

AUTOMATED CONSTRUCTION OF 3D STATISTICAL SHAPE MODELS

Automated segmentation of medical images is a difficult task because of the complexity of anatomic structures, inter-patient variability, and imperfect image acquisition. Prior knowledge, in the form of pointbased statistical shape models (point distribution models) of a structure of interest can greatly assist segmentation to robustly find the structure in a patient's image. Point distribution models are obtained through sets of corresponding landmarks lying on surfaces of training structures. The key to the automated construction of a three-dimensional (3D) statistical shape model is the identification of corresponding landmarks on training shapes, which is a challenging task. This paper presents a novel method for automated construction of 3D point distribution models. Corresponding surface points are obtained by two main steps: 1) volumes of interest (VOI), each containing one training structure, are manually defined, a reference structure is manually extracted from one training VOI and its surface is established and represented by a set of (reference) points, 2) reference landmarks are propagated to other training VOIs by transformations that are obtained by hierarchical elastic registration between the reference and each of the remaining training VOIs. We illustrate our approach using computed tomography data of the lumbar vertebra