Computed Image is too large when try to extract information from getSegmentData

[Xitermelon]

Hello,

Thank you for providing this powerful tool! It's a wonderful tool that helps me get vegetation cover change. However, I met some problems with "getSegmentData" function.
After I get the segment information by calling getSegmentData, I tried to extract them like max magnitude as training data for my feature collection using sampleRegions. However, it will always return "FetureCollection error: Computed Image is too large".
I've tried to clip my study area but it doesn't work.

Here is my code:
var ltgee = require('users/emaprlab/public:Modules/LandTrendr.js');

// define parameters
var startYear = 2000;
var endYear = 2019;
var startDay = '06-01';
var endDay = '09-30';
var aoi = ee.Geometry.Point(-67.9527,49.2603);

var index = 'NBR';
var ftvList = ['NBR'];
var runParams = {
maxSegments: 6,
spikeThreshold: 0.9,
vertexCountOvershoot: 3,
preventOneYearRecovery: true,
recoveryThreshold: 0.25,
pvalThreshold: 0.05,
bestModelProportion: 0.75,
minObservationsNeeded: 6
};
var maskThese = ['cloud', 'shadow', 'snow'];

// center and zoom the display in case outputs are to be mapped
Map.centerObject(aoi,10);
Map.addLayer(aoi);

// apply LandTrendr.js functions
var lt = ltgee.runLT(startYear, endYear, startDay, endDay, aoi, index, ftvList, runParams);

// get Segmentation data
var segInfo = ltgee.getSegmentData(lt, index, 'all');
var segDelta = segInfo.arraySlice(0, 4, 5).toArray(0);

var sortByThis = segDelta.toArray(0).multiply(-1);
var segInfoSorted = segInfo.arraySort(sortByThis);
var bigDelta = segInfoSorted.arraySlice(1,0,1);
var segMax = bigDelta.arraySlice(0,4,5);

var segMax2 = segMax.arrayProject([0]).arrayFlatten([['segMax']]);

// import traning data
var plots_with_class = ee.FeatureCollection('users/xichun_project/point_select_with_class');
print(plots_with_class);

var bands = ['segMax']
var training = segMax2.select(bands).sampleRegions({
collection: plots_with_class,
properties: ['Class'],
scale: 30
});
print(training);

Thank you in advance :)